Following is the list of past talks (by quarters) with titles, speakers, discussants, and relevant links. Click the subpages to view the same list with abstracts. See our homepage for future talks.
Tuesday, June 17, 2025: Julie Josse (PreMeDICaL Inria-Inserm & University of Montpellier)
- Title: Causal Alternatives to Meta-Analysis
- Discussant: Larry Han (Northeastern University)
- Abstract: Meta-analysis, by aggregating effect estimates from multiple studies conducted in diverse settings, stands at the top of the evidence-based medicine hierarchy. However, conventional approaches face key limitations: they often lack a clear causal interpretation, and practical constraints, such as data silos and regulatory barriers, frequently prevent access to individual-level data, limiting the feasibility of individual patient data meta-analyses.
In this talk, we introduce three causal alternatives to classical meta-analysis. First, we present novel causal aggregation formulas—distinct from fixed- and random-effects models—that are compatible with standard meta-analysis practices and do not require individual-level data but only aggregated data. While these often align with traditional conclusions, they can expose important discrepancies, including cases where conventional methods suggest benefit when a causal approach reveals harm.
Second, we connect federated learning with causal inference to enable treatment effect estimation from decentralized data sources. Finally, we discuss generalization methods aimed at predicting treatment effects in new target populations. Across these approaches, we focus on both absolute and relative measures, such as risk ratios and odds ratios, which pose unique challenges due to their non-linearity.
[Slides][Papers: #1, #2, #3, #4][Video][Discussant slides]
Tuesday, June 10, 2025: Matias Cattaneo (Princeton University)
- Title: Estimation and Inference in Boundary Discontinuity Designs
- Discussants: Alberto Abadie (MIT) and Kosuke Imai (Harvard University)
- Abstract: Boundary Discontinuity Designs are used to learn about treatment effects along a continuous boundary that splits units into control and treatment groups according to a bivariate score variable. These research designs are also called Multi-Score Regression Discontinuity Designs, a leading special case being Geographic Regression Discontinuity Designs. We study the statistical properties of commonly used local polynomial treatment effects estimators along the continuous treatment assignment boundary. We consider two distinct approaches: one based explicitly on the bivariate score variable for each unit, and the other based on their univariate distance to the boundary. For each approach, we present pointwise and uniform estimation and inference methods for the treatment effect function over the assignment boundary. Notably, we show that methods based on univariate distance to the boundary exhibit an irreducible large misspecification bias when the assignment boundary has kinks or other irregularities, making the distance-based approach unsuitable for empirical work in those settings. In contrast, methods based on the bivariate score variable do not suffer from that drawback. We illustrate our methods with an empirical application. Companion general-purpose software is provided.
[Slides][Paper][Video][Discussant 1 Slides][Discussant 2 Slides]
Tuesday, June 03, 2025: Edward Kennedy (Carnegie Mellon University)
- Title: Causal inference with high-dimensional treatments
- Discussant: Iván Díaz (New York University)
- Abstract: In this work we consider causal inference when the number of treatment levels is comparable to or larger than the number of observations. This setting brings two unique challenges: first, the treatment effects of interest are a high-dimensional vector rather than a low-dimensional scalar and, second, positivity violations are unavoidable. We first discuss fundamental limits of estimating effects in such settings, showing consistent estimation is impossible without further assumptions. We go on to propose new doubly robust thresholding/penalized estimators, and analyze them under exact and approximate sparsity assumptions. We then derive minimax lower bounds to characterize optimal rates of convergence. Finally we illustrate the methods in an education application studying school effects on test scores, where the number of treatments (schools) is in the thousands. This is joint work with Patrick Kramer (CMU) and Isaac Opper (RAND).
Tuesday, May 27, 2025: Francesco Locatello (Institute of Science and Technology Austria)
- Title: Powering causality with ML: Discovery, Representations, and Inference
- Discussant: Jason Hartford (University of Manchester & Valence Labs)
- Abstract: Machine learning and AI have the potential to transform data-driven scientific discovery, enabling not only accurate predictions for several scientific phenomena but also accelerating causal understanding. In this talk, I will show how machine learning has the potential to power causal analysis across 3 pillars: discovery, representations, and inference. I will discuss first how causal structure can be discovered using score matching approaches. Next, I will turn to a new interpretation of causal representation learning, introducing the measurement model as a way to conceptualize causal representations and the invariance principle as a unifying methodology. Finally, I will focus on powering causal inferences from high-dimensional observations. Using our real-world ISTAnt benchmark in experimental ecology as a motivating example, I will discuss challenges from selection bias during transfer learning and encoder bias due to pre-training and generalization across populations (e.g., multiple experiments with different treatments).
[Slides][Video][Discussion slides]
Tuesday, May 20, 2025: Luke Keele (University of Pennsylvania)
- Title: Clustered Observational Studies: A Review of Concepts and Methods
- Discussant: Eli Ben-Michael (Carnegie Mellon University)
- Abstract: The clustered observational study (COS) design is the observational counterpart to the clustered randomized trial. In a COS, a treatment is assigned to intact groups, and all units within the group are exposed to the treatment. However, the treatment is non-randomly assigned. COSs are common in both education and health services research. In education, treatments may be given to all students within some schools but withheld from all students in other schools. In health studies, treatments may be applied to clusters such as hospitals or groups of patients treated by the same physician. In this presentation, I provide a review of research on the COS design. I start with identification issues for COS designs. Specifically, I outline how this design is subject to differential selection, which occurs when the units' cluster selections depend on the clusters' treatment assignments. This can occur when units switch clusters in response to how the cluster was assigned to treatment. Early work on COSs made an implicit assumption that rules out the presence of differential selection. I review how causal effects can be identified in the presence of differential selection and how to reason about when it is likely. Next, I review how COS designs are often subject to common support violations. Such violations are likely to occur in a COS, especially with a small number of treated clusters. I outline how trimming to ensure common support holds can result in extremely narrow causal estimands that are unlikely to generalize to other contexts. I then review methods for covariate adjustment in COS design. Extant methods for covariate adjustment include specialized forms of matching and weighting. I review these different methods and outline current research that compares the performance of these different methods. I conclude with more recent work that has developed key results on the nature of omitted confounders. Throughout I motivate concepts and methods with a number of COS applications from education and health services research.
[Slides][Video][Discussant slides]
Tuesday, May 13, 2025: Sam Pimentel (UC Berkeley)
- Title: Design Sensitivity and Its Implications for Weighted Observational Studies
- Discussant: Jacob Dorn (University of Pennsylvania)
- Abstract: Sensitivity to unmeasured confounding is not typically a primary consideration in designing weighted treated-control comparisons in observational studies. We introduce a framework allowing researchers to optimize robustness to omitted variable bias at the design stage using a measure called design sensitivity. Design sensitivity, which describes the asymptotic power of a sensitivity analysis, allows transparent assessment of the impact of different weighted estimation strategies on sensitivity. We apply this general framework to two commonly-used sensitivity models, the marginal sensitivity model and the variance-based sensitivity model. By comparing design sensitivities, we interrogate how key features of weighted designs --- including definition of treatment, inclusion criteria, and handling of extreme weights --- impact robustness to unmeasured confounding, and how these impacts may differ for the two different sensitivity models. We illustrate the proposed framework on a study examining drivers of support for the 2016 Colombian peace agreement.
[Paper][Slides][Video][Discussant slides]
Tuesday, May 06, 2025: Zijian Guo (Rutgers University)
- Title: Multi-Source Learning with Minimax Optimization: From Adversarial Robustness to Causal Invariance
- Discussant: Kaizheng Wang (Columbia University)
- Abstract: Empirical risk minimization often fails to deliver reliable predictions when the target distribution deviates from source populations. This talk explores leveraging multi-source data to build models that generalize and transfer effectively to target domains. We propose a distributionally robust optimization framework that maximizes worst-case performance over a set of potential target distributions. We first introduce the definition of a distributionally robust prediction model and show that it can be expressed as a weighted average of the conditional outcome models from the source populations.
We then extend this framework to the transfer learning regime by incorporating a small number of labeled outcomes from the target domain to guide the robust optimization. This approach achieves faster convergence rates than models trained solely on labeled target data, while mitigating negative transfer.
Finally, we connect causal invariance learning to distributionally robust learning with negative weights. We show that allowing negative weights over environment-specific risks enables the identification of invariant causal models, despite introducing nonconvexity. We establish a necessary and sufficient condition for identifying the causal invariance model under additive interventions. Under this causal identification condition, we show that standard gradient-based methods can efficiently solve the resulting nonconvex optimization problem, with provable convergence rates depending on the sample size and number of iterations.
[Slides][Video]
Tuesday, Apr 29, 2025: Angela Zhou (USC Marshall)
- Title: Robust Fitted-Q-Evaluation and Iteration under Sequentially Exogenous Unobserved Confounders
- Discussant: Qingyuan Zhao (University of Cambridge)
- Abstract: Offline reinforcement learning is important in domains such as medicine, economics, and e-commerce where online experimentation is costly, dangerous or unethical, and where the true model is unknown. However, most methods assume all covariates used in the behavior policy’s action decisions are observed. Though this assumption, sequential ignorability/unconfoundedness, likely does not hold in observational data, most of the data that accounts for selection into treat- ment may be observed, motivating sensitivity analysis. We study robust policy evaluation and policy optimization in the presence of sequentially-exogenous unobserved confounders under a sensitivity model. We propose and analyze orthogonalized robust fitted-Q-iteration that uses closed-form solutions of the robust Bellman operator to derive a loss minimization problem for the robust Q function, and adds a bias-correction to quantile estimation. Our algorithm enjoys the computational ease of fitted-Q-iteration and statistical improvements (reduced dependence on quantile estimation error) from orthogonalization. We provide sample complexity bounds, insights, and show effectiveness both in simulations and on real-world longitudinal healthcare data of treating sepsis. In particular, our model of sequential unobserved confounders yields an online Markov decision process, rather than partially observed Markov decision process: we illustrate how this can enable warm-starting optimistic reinforcement learning algorithms with valid robust bounds from observational data.
[Paper][Slides][Video][Discussant slides]
Tuesday, Apr 22, 2025: Young researchers' seminar
Speaker 1: Drago Plečko (Columbia University)
- Title: Monotonicity in graphical causal models: an algorithmic approach
- Abstract: A longstanding debate between the proponents of the potential outcomes (PO) and the graphical models (GM) approach to causal inference concerns the identification of causal effects under shape constraints (such as monotonicity). Scholars in the PO framework have developed seminal results leveraging monotonicity constraints in practical applications, such as in identification of local average treatment effects (LATE). Various assertions have been made in the PO literature that the graphical approach may be inherently limited for encoding shape constraints. At the same time, the GM approach has successfully provided complete algorithms for non-parametric identification of causal effects. Some special cases, such as linear or additive-noise models, are also well understood within this approach. However, incorporating shape constraints such as monotonicity, has so far eluded its scope. In this talk, we discuss a recent advance which allows one to encode monotonicity constraints in a graphical model, and leverage monotonicity for identification.
[Slides][Video]
Speaker 2: Daiqi Gao (Harvard University)
- Title: Harnessing Causality in Online Reinforcement Learning for Digital Interventions
- Abstract: Mobile health leverages mobile devices to monitor and manage patient health through digital interventions. To improve the efficacy of interventions given user heterogeneity, we use online reinforcement learning (RL) to learn personalized intervention policies. Online learning in mobile health faces challenges due to noisy user behavior and limited user-specific data. To improve sample efficiency, we embed established behavioral theories via an expert-provided causal directed acyclic graph (DAG), which forms the assumptions in our RL algorithms. The causal DAG guides both state construction and reward design. Specifically, we consider bagged decision times, where each bag represents a finite sequence of consecutive decisions, and the transition dynamics are non-Markovian and non-stationary within the bag. Leveraging the conditional independencies implied by the DAG, we construct an optimal state that ensures Markov transitions within and across bags, maximizes the optimal value function across all state constructions, and minimizes variance in learning. When the DAG informs mediators between actions and rewards, we learn a surrogate reward online using these mediators and demonstrate that optimizing the surrogate reward improves the regret bound.
[Slides][Video]
Tuesday, Apr 15, 2025 [SCI+OCIS]: Gabriel Loewinger (NIH), Emre Kiciman (Microsoft), Natalie Levy (Aetion)Ga
- Title: Roundtable Panel – Exploring Career Paths in Pharma, Government, and Technology
[Video]
Tuesday, Apr 08, 2025: Nathan Kallus (Cornell University)
- Title: Learning Surrogate Indices from Historical A/Bs: Adversarial ML for Debiased Inference on Functionals of Ill-Posed Inverses
- Discussant: Rahul Singh (Harvard University)
- Abstract: Experimentation on digital platforms often faces a dilemma: we want rapid innovation but we also want to make decisions based on long-term impact. Usually one resorts to looking at indices (i.e., scalar-valued functions) that combine multiple short-term surrogate outcomes. Constructing indices by regressing long-term metrics on short-term ones is easy with off-the-self ML but suffers bias from confounding and direct (i.e., unmediated) effects. I will discuss how to instead leverage past experiments as instrumental variables (IVs) and some surrogates as negative-control outcomes, with real-world examples from Netflix. There are two key technical challenges to surmount to make this possible. First, past experiments characterize the right surrogate index as a solution to an ill-posed system of moment equations: it does not uniquely identify an index, and approximately solving it does not translate to approximating any solution. We tackle this by developing a novel debiasing method for inference on linear functionals of solutions to ill-posed problems (as average long-term effects are such functionals of the index) and adversarial ML estimators for the solution admitting flexible hypothesis classes, such as neural nets and reproducing kernel Hilbert spaces. Second, even as we observe more past experiments, we have non-vanishing bias in estimating the moment equation implied by each one, since each experiment has a bounded size that is often just barely powered to detect effects. We tackle this by incorporating an instrument-splitting technique into our estimators, leading to a ML analogue of the classic (linear) jackknife IV estimator (JIVE) with guarantees for flexible function classes in terms of generic complexity measures.
[Video][Discussant slides]
Tuesday, Apr 01, 2025: Dylan Small (University of Pennsylvania)
- Title: Exploration, Confirmation, and Replication in the Same Observational Study: A Two Team Cross-Screening Approach to Studying the Effect of Unwanted Pregnancy on Mothers’ Later Life Outcomes
- Discussant: Ying Jin (Harvard University)
- Abstract: The long term consequences of unwanted pregnancies carried to term on mothers have not been much explored. We use data from the Wisconsin Longitudinal Study (WLS) and propose a novel approach, namely two team cross-screening, to study the possible effects of unwanted pregnancies carried to term on various aspects of mothers’ later-life mental health, physical health, economic well-being and life satisfaction. Our method, unlike existing approaches to observational studies, enables the investigators to perform exploratory data analysis, confirmatory data analysis and replication in the same study– this is a valuable property when there is only a single data set available with unique strengths to perform exploratory, confirmatory and replication analysis. In two team cross-screening, the investigators split themselves into two teams and the data is split as well according to a meaningful covariate. Each team then performs exploratory data analysis on its part of the data to design an analysis plan for the other part of the data. The complete freedom of the teams in designing the analysis has the potential to generate new unanticipated hypotheses in addition to a prefixed set of hypotheses. Moreover, only the hypotheses that looked promising in the data each team explored are forwarded for analysis (thus alleviating the multiple testing problem). These advantages are demonstrated in our study of the effects of unwanted pregnancies on mothers’ later life outcomes. This is joint work with Samrat Roy, Marina Bogomolov, Ruth Heller, Tishra Beeson and Amy Claridge.
[Paper][Slides][Discussant slides][Video]
Tuesday, Mar 25, 2025: Guido Imbens (Stanford University)
- Title: Identification of nonparametric factor models for average treatment effects
- Discussant: Bryan Graham (UC Berkeley)
[Slides][Video][Discussant slides]
Tuesday, Mar 18, 2025: Alberto Abadie (MIT)
- Title: Synthetic Controls for Experimental Design
- Discussant: Dmitry Arkhangelsky (CEMFI)
[Paper][Slides][Video][Discussant slides]
Tuesday, Mar 11, 2025: Katherine A. Keith (Williams College)
- Title: Proximal Causal Inference with Text Data
- Discussant: Naoki Egami (Columbia University)
[Paper][Video][Slides]
Tuesday, Mar 04, 2025: Vasilis Syrgkanis (Stanford University)
- Title: Detecting clinician implicit biases in diagnoses using proximal causal inference
- Discussant: Ilya Shpitser (Johns Hopkins University)
[Paper][Slides][Video]
Tuesday, Feb 25, 2025: Young researchers' seminar
Speaker 1: Julius von Kügelgen (ETH Zürich)
- Title: Multi-Domain Causal Representation Learning
[1] L Wendong, A Kekić, J von Kügelgen, S Buchholz, M Besserve, L Gresele, B Schölkopf. Causal Component Analysis. In: Advances in Neural Information Processing Systems, 2023.
[2] J von Kügelgen, M Besserve, L Wendong, L Gresele, A Kekić, E Bareinboim, DM Blei, B Schölkopf. Nonparametric Identifiability of Causal Representations from Unknown Interventions. In: Advances in Neural Information Processing Systems, 2023.
[Slides][Video]
Speaker 2: David Bruns-Smith (Stanford University)
- Title: Augmented balancing weights as linear regression
[Paper][Slides][Video]
[SCI-OCIS joint webinar] Wednesday, February 19, 2025 @ 12:00 PM EST: Elizabeth A. Stuart (Johns Hopkins) [Registration link]
- Title: Lessons in "causality" from National Academies consensus panels
[Video]
Tuesday, Feb 18, 2025: Eric Tchetgen Tchetgen (University of Pennsylvania)
- Title: Revisiting Identification in the Binary Instrumental Variable Model: the NATE and Beyond
[Paper][Slides][Video]
Tuesday, Feb 11, 2025: Corwin Zigler (Brown University)
- Title: Causal health impacts of power plant emission controls under modeled and uncertain physical process interference
- Discussant: Fredrik Sävje (Uppsala University)
[Slides][Paper][Video][Discussant slides]
Tuesday, Feb 4, 2025: Michal Kolesár (Princeton University)
- Title: Evaluating Counterfactual Policies Using Instruments
- Discussant: Edward Vytlacil (Yale University)
[Slides][Video][Discussant slides]
Tuesday, Jan 28, 2025: Anne Helby Petersen (University of Copenhagen)
- Title: What are we discovering? Two perspectives on interpretable evaluation of causal discovery algorithms
- Discussant: Vanessa Didelez (Leibniz Institute for Prevention Research and Epidemiology)
[Slides][Video][Discussant slides]
Tuesday, Jan 21, 2025: Davide Viviano (Harvard University)
- Title: Program Evaluation with Remotely Sensed Outcomes
- Discussant: Hyunseung Kang (University of Wisconsin-Madison)
[Slides][Paper][Video][Discussant slides]
Tuesday, Jan 14, 2025: Qingyuan Zhao (University of Cambridge)
- Title: On statistical and causal models associated with acyclic directed mixed graphs
- Discussant: Thomas Richardson (University of Washington)
[Slides][Paper][Video][Discussant slides]
Tuesday, December 10, 2024: Panos Toulis and Wenxuan Guo (University of Chicago)
- Title: ML-assisted Randomization Tests for Complex Treatment Effects in A/B Experiments
- Discussant: Xinran Li (University of Chicago)
[Slides][Video][Discussant slides]
Tuesday, December 03, 2024: Yiqing Xu (Stanford University)
- Title: Factorial Difference-in-Differences
- Discussant: Erin Hartman (University of California Berkeley)
[Slides][Paper][Video][Discussant slides]
Tuesday, November 19, 2024: Jared S. Murray (University of Texas at Austin)
- Title: A Unifying Weighting Perspective on Causal Machine Learning: Kernel Methods, Gaussian Processes, and Bayesian Tree Models
- Discussant: Rahul Singh (Harvard University)
[Video]
Tuesday, November 12, 2024: Tianchen Qian (University of California Irvine)
- Title: Causal inference and machine learning in mobile health – modeling time-varying effects using longitudinal functional data
- Discussant: Walter Dempsey (University of Michigan)
[Slides][Video]
Tuesday, November 5, 2024 (Young researcher seminar)
Speaker 1: Jinzhou Li (Stanford University)
- Title: Root cause discovery via permutations and Cholesky decomposition
[Paper][Slides][Video]
Speaker 2: Yuyao Wang (University of California San Diego)
- Title: Learning treatment effects under covariate dependent left truncation and right censoring
[Slides][Video]
Tuesday, October 29, 2024: Toru Kitagawa (Brown University)
- Title: Policy Choice in Time-Series by Empirical Welfare Maximization
- Discussant: Mikkel Plagborg-Moller (Princeton University)
[Paper][Video][Slides][Discussant slides]
Tuesday, October 22, 2024: Alexis Bellot (Google DeepMind, London)
- Title: Partial Transportability for Domain Generalization
- Discussant: Adam Li (Columbia University)
[Paper][Video][Slides][Discussant slides]
Tuesday, October 15, 2024: Oliver Dukes (Ghent University)
- Title: Nonparametric tests of treatment effect homogeneity for policy-makers
- Discussant: Edward Kennedy (Carnegie Mellon University)
[Paper][Video][Slides][Discussion slides]
Tuesday, October 8, 2024 (Young researcher seminar)
Speaker 1: Philipp Faller (Karlsruhe Institute for Technology)
- Title: Self-compatibility: Evaluating causal discovery without ground truth
[Paper][Video][Slides]
Speaker 2: Bijan Mazaheri (Broad Institute of MIT and Harvard)
- Title: Synthetic Potential Outcomes and the Hierarchy of Causal Identifiability
[Paper][Video][Slides]
Tuesday, October 1, 2024: Anish Agarwal (Columbia University)
- Title: Synthetic Combinations: A Causal Inference Framework for Combinatorial Interventions
- Discussant: Christina Lee Yu (Cornell University)
[Paper][Video] [Slides] [Discussion slides]
Tuesday, June 4, 2024: Wang Miao (Peking University)
- Title: Introducing the specificity score: a measure of causality beyond P value
- Discussant: Qingyuan Zhao (University of Cambridge)
[Video]
Tuesday, May 28, 2024: Rodrigo Pinto (UCLA)
- Title: What is causality? How to express it? And why it matters
- Discussant: Ilya Shpitser (Johns Hopkins University)
[Video] [Slides] [Discussant slides]
Tuesday, May 21, 2024 (Young researcher seminar)
- Speaker 1: Abhin Shah (MIT)
- Title: On counterfactual inference with unobserved confounding via exponential family
[Video] [Related papers: #1, #2]
- Speaker 2: Brian Gilbert (New York University)
- Title: Identification and estimation of mediational effects of longitudinal modified treatment policies
[Video] [Related paper]
Tuesday, May 7, 2024 : Raaz Dwivedi (Cornell University)
- Title: Integrating Double Robustness into Causal Latent Factor Models
- Discussant: James Robins (Harvard University)
[Video] [Slides] [Related paper #1 #2]
Tuesday, April 30, 2024: Hyunseung Kang (University of Wisconsin-Madison)
- Title: Transfer Learning Between U.S. Presidential Elections: How much can we learn from a 2020 ad campaign to inform 2024 elections?
- Discussant: Melody Huang (Harvard University)
[Video] [Slides]
Tuesday, April 23, 2024 (Young researcher seminar)
- Speaker 1: Chan Park (University of Pennsylvania)
- Title: Single Proxy Control
[Video] [Slides]
- Speaker 2: Andrew Yiu (University of Oxford)
- Title: Semiparametric posterior corrections
[Video] [Slides]
Tuesday, April 16, 2024: Mihaela van der Schaar (University of Cambridge)
- Title: The (Causal) Discovery Ladder: Unravelling Governing Equations and Beyond using Machine Learning
[Video] [Slides] [Related paper: #1, #2, #3, #4]
Tuesday, April 9, 2024: Chao Ma (Microsoft Research)
- Title: Towards Causal Foundation Model: on Duality between Causal Inference and Attention
- Discussant: Jiaqi Zhang (MIT)
[Video]
Tuesday, April 2, 2024: Kosuke Imai (Harvard University)
- Title: The Cram Method for Efficient Simultaneous Learning and Evaluation
- Discussant: Rui Song (North Carolina State University) and Hengrui Cai (UC Irvine) - Q&A moderator: Michael Li (Harvard University)
[Video] [Slides] [Discussion slides]
Tuesday, March 26, 2024: Krikamol Muandet (CISPA)
- Title: A Measure-Theoretic Axiomatisation of Causality
- Discussant: Ricardo Silva (UCL)
- Q&A moderator: Junhyung Park (Max Planck Institute)
[Video] [Slides] [Paper]
Tuesday, March 19, 2024: Sara Magliacane (University of Amsterdam, MIT-IBM Watson AI Lab), Phillip Lippe (University of Amsterdam)
- Title: BISCUIT: Causal Representation Learning from Binary Interactions
- Discussant: Sébastien Lachapelle (Samsung SAIL)
[Video] [Slides] [Part-2 slides]
Tuesday, March 12, 2024: David Lagnado (UCL)
- Title: Causality in Mind: Learning, Reasoning and Blaming
- Discussant: Neil Bramley (University of Edinburgh)
[Video] [Slides]
Tuesday, March 5, 2024 (young researcher seminar)
- Speaker 1: Xinwei Shen (ETH Zurich)
- Title: Causality-oriented robustness: exploiting data heterogeneity at different levels
[Video] [Slides]
- Speaker 2: Giulio Grossi (University of Florence)
- Title: SMaC: Spatial Matrix Completion method
[Video] [Slides]
Tuesday, February 27, 2024: Maria Glymour (Boston University)
- Title: Evidence triangulation in dementia research
- Discussant: George Davey Smith (University of Bristol)
[Video]
Tuesday, February 20, 2024: Iván Díaz (New York University)
- Title: Recanting twins: addressing intermediate confounding in mediation analysis
- Discussant: Daniel Malinsky (Columbia University)
[Video] [Slides] [Discussant slides]
Tuesday, February 13, 2024: Ting Ye (University of Washington)
- Title: Debiased Multivariable Mendelian Randomization
- Discussant: Neil Davies (UCL)
[Video] [Slides]
Tuesday, February 6, 2024: Fan Yang (Tsinghua University)
- Discussant: Xiaohua (Andrew) Zhou (Peking University)
- Title: Mediation analysis with the mediator and outcome missing not at random
[Video] [Slides]
Tuesday, January 30, 2024: Sarah Robertson (in place of Issa Dahabreh) (Harvard University)
- Discussant: Paul Zivich (University of North Carolina at Chapel Hill)
- Title: Transporting inferences about intention-to-treat effects and per-protocol effects when there is non-adherence
Tuesday, January 23, 2024: Victor Veitch (University of Chicago)
- Discussant: Francesco Locatello (Institute of Science and Technology Austria)
- Title: Linear Structure of High-Level Concepts in Text-Controlled Generative Models, and the role of Causality
[Video] [Slides]
Tuesday, January 16, 2024: Jonas Peters (ETH Zurich), joint with Nicola Gnecco (UCB) and Sorawit Saengkyongam (ETH Zurich)
- Title: On Invariance-based Generalization and Extrapolation
[Video] [Slides]
Tuesday, January 9, 2024: Elizabeth Tipton (Northwestern University)
- Discussant: Andrew Gelman (Columbia University)
- Title: Designing Randomized Trials to Predict Treatment Effects
[Video] [Slides]
Tuesday, December 12, 2023: Mats Stensrud (EPFL) and Aaron Sarvet (EPFL)
- Title: Interpretational errors in causal inference and how to avoid them
- Discussant: Kerollos Wanis (Western University) and Vanessa Didelez (BIPS) . Q&A moderators: Lan Wen (U Waterloo)
[Video] [Slides] [Paper]
Tuesday, December 5, 2023: Erica Moodie (McGill University)
- Title: Flexible modeling of adaptive treatment strategies for censored outcomes
- Discussant: Yu Cheng (University of Pittsburgh), Peter Thall (MD Anderson)
[Video] [Slides]
Tuesday, November 28, 2023: Yuqi Gu (Columbia University)
Title: Identifiable Deep Generative Models for Rich Data Types with Discrete Latent Layers
Discussant: Qingyuan Zhao (University of Cambridge)
[Video] [Slides]
Tuesday, November 14, 2023: Maya Mathur (Stanford University)
Title: A common-cause principle for eliminating selection bias in causal estimands through covariate adjustment
Discussant: Eric Tchetgen Tchetgen (University of Pennsylvania) and Nan Laird (Harvard University) [new format]
[Video] [Slides] [Paper]
Tuesday, November 7, 2023: Anish Agarwal (Columbia University)
Title: On Causal Inference with Temporal and Spatial Spillovers in Panel Data
Discussant: Iavor Bojinov (Harvard University) and Ashesh Rambachan (MIT)
[Video] [Slides] [Paper #1, #2]
Tuesday, October 31, 2023: Richard Guo (University of Cambridge)
Title: Confounder selection via iterative graph expansion
Discussant: Ilya Shpitser (Johns Hopkins University)
[Video] [Slides] [Discussant slides] [Paper]
Tuesday, October 24, 2023 (Young researcher seminar)
- Speaker1: Michael Celentano (UC Berkeley)
Title: Challenges of the inconsistency regime: Novel debiasing methods for missing data models
[Video] [Slides]
- Speaker 2: Chris Harshaw (UC Berkeley + MIT)
Title: Clip-OGD: An Experimental Design for Adaptive Neyman Allocation in Sequential Experiments
[Video] [Slides] [Paper]
Tuesday, October 17, 2023: Ricardo Silva (University College London)
Title: Intervention Generalization: A View from Factor Graph Models
Discussant: Anish Agarwal (Columbia University)
[Video] [Slides] [Discussant slides] [Paper]
Tuesday, October 10, 2023: Ruoqi Yu (UIUC) Q&A moderator: Peng Ding (UC Berkeley)
Title: How to learn more from observational factorial studies
Discussant [new format]: José Zubizarreta (Harvard) and Luke Keele (UPenn)
[Video]
Tuesday, October 3, 2023: Caleb Miles (Columbia University)
Title: Two fundamental problems in causal mediation analysis
Discussant [standard form]: James Robins (Harvard University) and Thomas Richardson (University of Washington)
[Video] [Slides] [Discussant slides]
Tuesday, September 26, 2023: Michael Hudgens and Chanhwa Lee (University of North Carolina at Chapel Hill)
Title: Efficient Nonparametric Estimation of Stochastic Policy Effects with Clustered Interference
Discussants: Hyunseung Kang (UW-Madison), Chris Harshaw (MIT, Berkeley)
[Video] [Slides] [Paper]
Tuesday, September 19, 2023: Andrew Gelman (Columbia University)
Title: Better Than Difference in Differences
Discussants: Elizabeth Tipton (Northwestern), Avi Feller (Berkeley), Jonathan Roth (Brown), Pedro Sant'Anna (Emory)
[Video] [Paper]
Tuesday, June 6, 2023: Erin Gabriel (University of Copenhagen)
Title: Derivation and usefulness of tight symbolic causal bounds for measures of benefit in observational and imperfect randomized studies with ordinal outcomes
Discussant: Michael Fay (NIH/NIAID)
[Video]
Tuesday, May 30, 2023
- Student speaker 1: Benedicte Colnet (INRIA)
Title: Risk ratio, odds ratio, risk difference... Which causal measure is easier to generalize?
[Slides] [Video]
- Student speaker 2: Keegan Harris (CMU)
Title: Strategyproof Decision-Making in Panel Data Settings
[Slides] [Video]
Tuesday, May 23, 2023: Richard Samworth (University of Cambridge)
- Title: Optimal nonparametric testing of Missing Completely At Random, and its connections to compatibility
- Discussant: Yixin Wang (University of Michigan)
[Recording] [Slides]
Tuesday, May 9, 2023: Yuhao Wang (Tsinghua University)
Title: Root-n-consistent estimators for average treatment effect with minimal sparsity
Discussant: Rajarshi Mukherjee (Harvard University)
[Video] [Slides] [Discussant slides] [Related paper #1, #2]
Tuesday, May 2, 2023: M. (Thijs) van Ommen (Utrecht University)
Title: Graphical Representations for Algebraic Constraints of Linear Structural Equations Models
Discussant: Rohit Bhattacharya (Williams College)
[Video] [Slides] [Discussant slides]
Tuesday, April 25, 2023: Matthew Gentzkow (Stanford University)
Title: Causal Interpretation of Structural IV Estimands
Discussant: Peter Hull (Brown University)
Tuesday, April 18, 2023: Philipp Bach and Sven Klaassen (University of Hamburg)
Title: (Tutorial) DoubleML - A state-of-the-art framework for double machine learning in Python and R
[Video] [Slides] [Website] [Paper]
Tuesday, April 11, 2023: Niels Richard Hansen (University of Copenhagen)
Title: Cyclic graphical models and causal learning
Discussant: Patrick Forré (University of Amsterdam)
[Recording] [Slides]
Tuesday, April 4, 2023: Interview with Philip Dawid
Interviewer: Vanessa Didelez (BIPS Leibniz Institute)
[Video]
Tuesday, March 28, 2023: Robin Evans (University of Oxford)
Title: Parameterizing and Simulating from Causal Models
Discussant: Larry Wasserman (CMU)
[Video] [Slides] [Discussant slides]
Tuesday, March 21, 2023: Jessica Young (Harvard University)
Title: Causal inference with competing events
Discussant: Jacqueline Rudolph (Johns Hopkins University), Q&A moderator: Mats Stensrud (EPFL)
[Video] [Slides] [Discussant slides]
Tuesday, March 14, 2023: Student talks
- Student speaker 1: Melody Huang (UC Berkeley)
Title: Variance-based sensitivity analysis for weighting estimators results in more informative bounds
[Slides]
- Student speaker 2: Tobias Freidling (University of Cambridge)
Title: Sensitivity Analysis with the R^2-calculus
[Slides] [Video]
Tuesday, March 7, 2023: Sofia Triantafyllou (University of Crete)
Title: A Bayesian Method for Causal Effect Estimation with Observational and Experimental Data
Discussant: Shu Yang (NC State University)
[Video] [Slides] [Discussant slides]
Tuesday, February 28, 2023: Christina Yu (Cornell University)
- Title: Exploiting Neighborhood Interference with Low Order Interactions under Unit Randomized Design
- Discussant: Chencheng Cai (Temple University & Harvard University)
- Q&A moderator: Mayleen Cortez and Matt Eichhorn (Cornell University)
[Video] [Slides] [Discussant slides]
Tuesday, February 21, 2023: Ingeborg Waernbaum (Uppsala University)
Title: Selection bias and multiple inclusion criteria in observational studies
Discussant: Maya Mathur (Stanford University), Q&A moderator: Stina Zetterström (Uppsala University)
[Video] [Slides] [Discussant slides]
Tuesday, February 14, 2023: Stijn Vansteelandt (Ghent University)
Title: Assumption-lean Causal Modeling
Discussant: Elizabeth Ogburn (Johns Hopkins University)
[Video] [Slides] [Discussant slides] [Paper 1, 2, 3]
Tuesday, February 7, 2023: Lauren Dang (UC Berkeley)
Title: Integration of Observational and Randomized Controlled Trial Data: Approaches, Challenges, A Novel Estimator, and Application to the LEADER Cardiovascular Outcomes Trial
[Video] [Paper] [Slides] [Discussant slides]
Tuesday, January 31, 2023: Issa Kohler-Hausmann & Lily Hu (Yale University)
Title: Causal mediators and misdefined causal quantities
Tuesday, January 24, 2023: Issa Kohler-Hausmann & Lily Hu (Yale University)
Title: What is the causal effect an effect of in audit/correspondence studies?
Tuesday, January 17, 2023: Moritz Hardt (Max Planck Institute for Intelligent Systems)
Title: From prediction to power
Discussant: Michael P. Kim (UC Berkeley)
[Video] [Slides]
Tuesday, January 10, 2023: Ilya Shpitser (Johns Hopkins University)
Title: Fairness By Causal Mediation Analysis: Criteria, Algorithms, and Open Problems
Discussant: Ricardo Silva (University College London)
[Video] [Slides] [Discussant slides]
Tuesday, December 6, 2022: Jose Zubizarreta (Harvard University)
Title: Bridging Matching, Regression, and Weighting as Mathematical Programs for Causal Inference
Discussant: Mike Baiocchi (Stanford University)
[Video]
Tuesday, November 29, 2022: Wayne Lam (CMU)
Title: Greedy Relaxations of the Sparsest Permutation Algorithm
Discussant: Alex Markham (KTH Royal Institute of Technology)
[Video] [Slides] [Discussant slides] [Project]
Tuesday, November 22, 2022: Julie Josse (Inria)
Title: Causal inference for brain trauma: leveraging incomplete observational data and RCT
Discussant: Elizabeth Stuart (Johns Hopkins University)
[Video] [Slides] [Paper #1, #2, #3]
Tuesday, November 15, 2022: Karthik Rajkumar (LinkedIn)
Title: A causal test of the strength of weak ties
Discussant: Dean Eckles (MIT)
[Video] [Paper] [Slides] [Discussant slides]
Tuesday, November 8, 2022: Luke Miratrix (Harvard University)
Title: A devil’s bargain? Repairing a Difference in Differences parallel trends assumption with an initial matching step
Discussant: Laura Hatfield (Harvard University)
[Video] [Slides] [Discussant slides]
Tuesday, November 1, 2022: Kun Zhang (CMU)
Title: Methodological advances in causal representation learning
Discussant: Victor Veitch (University of Chicago)
[Video] [Slides]
Tuesday, October 25, 2022: Rahul Singh (MIT) & Jiaqi Zhang (MIT)
Talk 1 (Rahul Singh) Title: Causal Inference with Corrupted Data: Measurement Error, Missing Values, Discretization, and Differential Privacy
[Video] [Slides]
Talk 2 (Jiaqi Zhang) Title: Active Learning for Optimal Intervention Design in Causal Models
[Video] [Slides]
Tuesday, October 18, 2022: Biwei Huang (UC San Diego)
Title: Latent Hierarchical Causal Structure Discovery with Rank Constraints
Discussant: Erich Kummerfeld (University of Minnesota)
[Video] [Slides] [Discussion slides] [Paper]
Tuesday, October 11, 2022: Fan Li (Duke University)
Title: A tutorial on Bayesian causal inference
[Video] [Slides]
Tuesday, October 4, 2022: Lihua Lei (Stanford University)
Title: Double-Robust Two-Way-Fixed-Effects Regression For Panel Data
Discussant: Jeffrey Wooldridge (Michigan State University)
[Video] [Slides] [Discussant slides] [Paper]
Tuesday, September 27, 2022: Vasilis Syrgkanis (Stanford University)
Title: Automatic Debiased Machine Learning for Dynamic Treatment Effects and General Nested Functionals
Discussant: Eric Tchetgen Tchetgen (University of Pennsylvania)
[Video] [Slides] [Paper]
Tuesday, September 20, 2022: Dominik Janzing (Amazon Research)
Title: Formal framework for quantitative Root Cause Analysis
Discussant: Niklas Pfister (University of Copenhagen)
[Video] [Slides] [Discussant slides] [Paper #1, #2, #3]
Tuesday, June 28, 2022: Samuel Wang (Cornell University)
Title: Uncertainty Quantification for Causal Discovery
Discussant: Daniel Malinsky (Columbia University)
[Video] [Slides] [Discussant slides]
Tuesday, June 21, 2022: Geneviève Lefebvre (Université du Québec à Montréal)
Title: Bayesian joint modeling for causal mediation analysis with a binary outcome and a binary mediator
Discussant: Olli Saarela (University of Toronto)
[Slides] [Discussant slides]
Tuesday, June 14, 2022: AmirEmad Ghassami (Johns Hopkins University)
Title: Combining Experimental and Observational Data for Identification and Estimation of Long-Term Causal Effects
Discussant: Guido Imbens
[Video] [Slides] [Discussant slides] [Paper]
Tuesday, June 7, 2022: Mona Azadkia (ETH)
Title: A Fast Non-parametric Approach for Causal Structure Learning in Polytrees
Discussant: Bryon Aragam (Chicago Booth)
[Video] [Slides] [Paper]
Tuesday, May 31, 2022: Bin Yu (UC Berkeley)
Title: Predictability, stability, and causality with a case study to find genetic drivers of a heart disease
Discussant: Jas Sekhon (Yale University)
[Slides] [Video]
Tuesday, May 17, 2022: Mireille Schnitzer (University of Montreal)
Title: Estimands and estimation of COVID-19 vaccine effectiveness under the test-negative design: connections to causal inference
Discussant: David Benkeser (Emory University)
[Slides] [Video]
Tuesday, May 10, 2022: Tim Morrison (Stanford University); Harrison Li (Stanford University)
Talk #1: Optimality in multivariate tie-breaker designs
Talk #2: A general characterization of optimal tie-breaker designs
[Video 1] [Video 2] [Speaker 1 slides] [Speaker 2 slides]
Tuesday, May 3, 2022: Tyler VanderWeele (Harvard University)
Title: Causal Inference and Measure Construction: Towards a New Model of Measurement
Discussant: Fredrik Sävje (Yale University)
[Video] [Slides] [Discussant slides]
Tuesday, April 26, 2022: Shu Yang (NCSU)
Test-based integrative analysis for heterogeneous treatment effects combining randomized trial and real-world data
Discussant: Issa Dahabreh (Harvard University)
[Video] [Slides] [Discussant slides] [Paper]
Tuesday, April 19, 202: Alex Luedtke (University of Washington)
Adversarial Monte Carlo Meta-Learning of Conditional Average Treatment Effects
Discussant: Jonas Metzger (Stanford University)
[Video] [Discussant slides] [Slides]
Tuesday, April 12, 2022: Neil Davies (University of Bristol)
Average causal effect estimation via instrumental variables: the no simultaneous heterogeneity assumption
Discussant: Eric Tchetgen Techetgen
[Video] [Slides] [Discussant slides] [Paper]
Tuesday, April 5, 2022: Zijian Guo (Rutgers University)
Two Stage Curvature Identification with Machine Learning: Causal Inference with Possibly Invalid Instrumental Variables
Discussant: Frank Windmeijer (University of Oxford)
[Video] [Slides] [Discussant slides] [Paper]
Tuesday, March 29, 2022: Shuangning Li (Stanford University); Michael Oberst (MIT)
Talk #1: Random Graph Asymptotics for Treatment Effect Estimation under Network Interference
[Video] [Slides]
Talk #2: Regularizing towards Causal Invariance: Linear Models with Proxies
[Video] [Slides]
Tuesday, March 22, 2022: Mathias Drton (Technical University of Munich)
Half-Trek Criterion for Identifiability of Latent Variable Models
Discussant: Robin Evans (University of Oxford)
[Video] [Slides] [Discussant slides] [paper]
Tuesday, March 15, 2022: Chengchun Shi (LSE)
A reinforcement learning framework for dynamic causal effects evaluation in A/B testing
Discussant: Will Wei Sun (Purdue University)
[Video] [Slides] [Discussant slides]
Tuesday, March 8, 2022: Yuansi Chen (Duke University)
Domain adaptation under structural causal models
Discussant: Biwei Huang (CMU)
[Video] [Slides] [Discussion slides] [Paper]
Tuesday, March 1, 2022: Kosuke Imai (Harvard University)
Safe Policy Learning through Extrapolation: Application to Pre-trial Risk Assessment
Discussant: Yifan Cui (National University of Singapore)
[Video] [Slides] [Discussant slides] [Paper]
Tuesday, February 22, 2022: Dominik Rothenhäusler (Stanford University)
Calibrated inference: statistical inference that accounts for both sampling uncertainty and distributional uncertainty
Discussant: Guido Imbens (Stanford University)
[Video] [Slides]
Tuesday, February 15, 2022: Luke Keele (University of Pennsylvania)
So Many Choices: The Comparative Performance of Statistical Adjustment Methods
Discussant: Iván Díaz (Cornell University)
[Video] [Slides] [Discussant slides]
Tuesday, February 8, 2022: Zhimei Ren (University of Chicago)
Sensitivity Analysis of Individual Treatment Effects: A Robust Conformal Inference Approach
Discussant: Stefan Wager (Stanford University)
[Video] [Slides] [Discussant slides] [Paper]
Tuesday, February 1 , 2022: Sander Beckers (University of Tübingen)
Causal Sufficiency and Actual Causation
Discussant: Thomas Icard (Stanford University)
[Video] [Slides] [Discussant slides] [Paper]
Tuesday, January 25, 2022: Daniel McCaffrey (ETS)
Nonrandom Samples and Causal Inference
Discussant: Shu Yang (North Carolina State University)
[Video] [Slides] [Discussant slides]
Tuesday, January 18, 2022: Sach Mukherjee (University of Cambridge)
A machine learning approach for causal structure estimation in high dimensions
Discussant: Yuhao Wang (Tsinghua University)
[Discussant slides]
Tuesday, January 11, 2022: Interview with Guido Imbens (Stanford University)
[Video]
Tuesday, December 14, 2021: Ruoxuan Xiong (Emory University)
Efficient Treatment Effect Estimation in Observational Studies under Heterogeneous Partial Interference
Discussant: Fabrizia Mealli (University of Florence)
[Video] [Slides] [Discussant Slides]
Tuesday, December 7, 2021: Jann Spiess (Stanford University)
Improving Inference from Simple Instruments through Compliance Estimation
Discussant: Damian Kozbur (University of Zurich)
[Video] [paper] [slides] [Discussant slides]
Tuesday, November 30, 2021: Thomas Richardson (University of Washington)
Single World Intervention Graphs: A simple framework for unifying graphs and potential outcomes with applications to mediation analysis
Discussant: Mats Stensrud (EPFL)
[Video] [Slides] [Discussant slides]
Tuesday, November 16, 2021: Linbo Wang (University of Toronto)
Causal inference on distribution functions
Discussant: Hongtu Zhu (University of North Carolina at Chapel Hill)
[Paper] [Video] [Slides] [Discussant slides]
Tuesday, November 9, 2021: Jin Tian (Iowa State University)
Estimating Identifiable Causal Effects through Double Machine Learning - Graph-based & Data-driven Approaches
Discussant: Ilya Shpitser (John Hopkins University)
[Video] [Paper #1, #2] [Slides] [Discussant slides]
Tuesday, November 2, 2021: Xinran Li (UIUC)
Randomization Inference beyond the Sharp Null: Bounded Null Hypotheses and Quantiles of Individual Treatment Effects
Discussant: Panos Toulis (Chicago Booth)
[Video] [Slides] [Discussant slides]
Tuesday, October 26, 2021: Carlos Cinelli (University of Washington)
Transparent and Robust Causal Inference in the Social and Health Sciences
Discussant: Guido Imbens (Stanford)
[Video] [Paper #1, #2, #3] [Slides] [Discussant slides]
Tuesday, October 19, 2021: Juan Correa (Columbia University & Universidad Autónoma de Manizales) & Nicola Gnecco (University of Geneva)
Talk1: Generalizing the Effect of Soft Interventions [Video] [Slides]
Talk2: Causal discovery in heavy-tailed models [Video] [Slides]
Tuesday, October 12, 2021: Colin Fogarty (MIT)
Prepivoting in Finite Population Causal Inference
Discussant: Tirthankar Dasgupta (Rutgers)
[Video] [Slides] [Discussant slides]
Tuesday, October 5, 2021: Eleanor Sanderson (University of Bristol)
Estimation of causal effects of an exposure at multiple time points through Multivariable Mendelian randomization
Discussant: Stephen Burgess (University of Cambridge)
[Video] [Slides] [Discussant slides]
Tuesday, September 28, 2021: Youjin Lee (Brown University)
Evidence factors from multiple, possibly invalid, instrumental variables
Discussant: Jose Zubizarreta (Harvard University)
[Video] [Slides] [Discussant slides]
Tuesday, September 21, 2021: Ted Westling (University of Massachusetts, Amherst)
Nonparametric tests of the causal null with non-discrete exposures
Discussant: Oliver Dukes (University of Pennsylvania)
[Video] [Paper] [Slides] [Discussant slides]
Tuesday, September 14, 2021: Daniel Malinsky (Columbia University)
Explaining the Behavior of Black-Box Prediction Algorithms with Causal Learning
Discussant: Joshua Loftus (LSE)
[Video] [Paper] [Slides] [Discussant slides]
Tuesday, September 7, 2021: Joseph Antonelli (University of Florida)
Heterogeneous causal effects of neighborhood policing in New York City with staggered adoption of the policy
Discussant: Matthew Cefalu (RAND Corporation)
[Video] [Paper] [Slides]
Tuesday, August 31, 2021: Susan Athey and Stefan Wager (Stanford)
Estimating heterogeneous treatment effects in R
[Video] [Athey Slides] [Wager Slides]
Tuesday, August 10, 2021: Maggie Makar (University of Michigan); Xiaojie Mao (Tsinghua University)
Talk #1: Causally motivated shortcut removal using auxiliary labels (Maggie Makar)
[Video] [Slides]
Talk #2: Controlling for Unmeasured Confounding in Panel Data Using Minimal Bridge Functions: From Two-Way Fixed Effects to Factor Models (Xiaojie Mao)
[Video] [Paper] [Slides]
Tuesday, August 3, 2021: Anish Agarwal (MIT) and Dennis Shen (Berkeley)
Synthetic Interventions
Discussant: Jason Poulos (Harvard)
[Video] [Paper] [Slides] [Discussant slides]
Tuesday, July 27, 2021: Johannes Textor (Radboud University)
Causal Inference using the R package DAGitty
[Video] [Slides]
Tuesday, July 20, 2021: Fiammetta Menchetti (Universita degli Studi di Firenzi); Armeen Taeb (ETH Zürich)
Talk 1: Estimating the causal effect of an intervention in a time series setting: the C-ARIMA approach (Fiammetta Menchetti)
[Video] [Slides]
Talk 2: Perturbations and causality in Gaussian latent variable models (Armeen Teb)
[Video] [Slides]
Tuesday, July 13, 2021: Alexander Volfovsky (Duke University)
Online experimentation for studying political polarization
Discussant: Edo Airoldi (Temple University)
[Video] [Paper #1] [Paper #2] [Slides] [Discussant slides]
Tuesday, July 6, 2021: Isaiah Andrews (Harvard University)
Inference on Winners
Discussant: Will Fithian (UC Berkeley)
[Video] [Paper] [Slides] [Discussant slides]
Tuesday, June 29, 2021: Sam Pimentel (UC Berkeley)
Optimal tradeoffs in matched designs comparing US-trained and internationally-trained surgeons.
Discussant: Magdalena Bennett (UT Austin)
[Video] [Paper] [Slides]
Tuesday, June 22, 2021: Stefan Wager (Stanford University)
Treatment Effects in Market Equilibrium (joint work with Evan Munro and Kuang Xu)
Discussant: Fredrik Sävje (Yale University)
[Video] [Slides] [Discussant slides]
Tuesday, June 15, 2021: Guido Imbens (Stanford University)
Using Experiments to Correct for Selection in Observational Studies
Discussant: Nathan Kallus (Cornell University)
[Video] [Slides] [Discussant slides]
Tuesday, June 8, 2021: Leon Bottou (Facebook)
Learning Representations Using Causal Invariance
Discussant: Dominik Rothenhäusler (Stanford University)
[Video]
Tuesday, June 1, 2021: Niklas Pfister (University of Copenhagen)
Statistical Testing under Distributional Shifts
Discussant: Thomas Berrett (University of Warwick)
[Video] [Paper] [Slides]
Tuesday, May 25, 2021: Razieh Nabi (Johns Hopkins University)
Semiparametric inference for causal effects in graphical models with hidden variables
Discussant: Eric Tchetgen Tchetgen (University of Pennsylvania)
[Video] [Slides] [Discussant slides]
Tuesday, May 18, 2021: Speaker: Ramesh Johari (Stanford University)
Experimental design in two-sided platforms: an analysis of bias
Discussant: Panos Toulis (University of Chicago)
[Video] [Paper] [Slides] [Discussant slides]
Tuesday, May 11, 2021: Corwin Zigler (University of Texas at Austin)
Bipartite inference and air pollution transport: estimating health effects of power plant interventions
Discussant: Forrest Crawford (Yale)
[Video] [Paper] [Slides]
Tuesday, May 4, 2021: Sara Magliacane (University of Amsterdam, MIT-IBM Watson AI Lab)
Domain adaptation by using causal inference to predict invariant conditional distributions
Discussant: Dominik Rothenhäusler (Stanford University)
[Video] [Paper] [Slides] [Discussant slides]
Tuesday, April 27, 2021: Issa Dahabreh (Harvard University)
Causally interpretable meta-analysis: transporting inferences from multiple randomized trials to a target population
Discussant: Eloise Kaizar (The Ohio State University)
[Slides] [Discussant slides]
Tuesday, April 20, 2021: Alberto Abadie (MIT)
A Penalized Synthetic Control Estimator for Disaggregated Data
Discussant: Stefan Wager (Stanford)
[Video] [Paper] [Slides]
Tuesday, April 13, 2021: Andrea Rotnitzky (Di Tella University, Buenos Aires)
Optimal adjustment sets in non-parametric graphical models
Discussant: Ema Perkovic (University of Washington)
[Video] [Slides] [Discussant slides]
Tuesday, April 6, 2021: Richard Berk (University of Pennsylvania)
Firearm Sales in California Through the Myopic Vision of an Interrupted Time Series Causal Analysis
Discussant: John Donohue (Stanford)
[Video] [Slides] [Discussant slides]
Tuesday, March 30, 2021: Elizabeth Stuart (Johns Hopkins University)
Using stacked comparative interrupted time series to estimate opioid policy effects
Discussant: Laura Hatfield (Harvard)
[Slides]
Tuesday, March 23, 2021: Joshua Angrist (MIT)
Simple and Credible Value-Added Estimation Using Centralized School Assignment
Discussant: Jesse Rothstein (UC Berkeley)
Joint with Peter Hull, Parag Pathak, Christopher Walters
[Paper] [Slides] [Discussant slides]
Tuesday, March 16, 2021: Kun Zhang (Carnegie Mellon)
"Learning and Using Causal Representations"
Discussant: Cosma Shalizi (Carnegie Mellon)
[Video] [Slides] [Discussant slides]
Tuesday, March 9, 2021: Luke Keele (University of Pennsylvania)
"Hospital Quality Risk Standardization via Approximate Balancing Weights"
Discussant: Sam Pimentel (UC Berkeley)
[Video] [Paper] [Slides] [Discussant slides]
Tuesday, March 2, 2021: Fredrik Sävje (Yale)
"Balancing covariates in randomized experiments using the Gram-Schmidt Walk"
Discussant: Peng Ding (UC Berkeley)
[Video] [Slides] [Paper] [Discussant slides]
Tuesday, February 23, 2021: Fan Li (Duke University)
"Causal Mediation Analysis for Sparse and Irregular Longitudinal Data"
Discussant: Georgia Papadogeorgou (University of Florida)
[Video] [Slides] [Discussant slides]
Tuesday, February 16, 2021: Donald Green (Columbia University)
"Using Placebo-Controlled Designs to Detect Edutainment Effects and Spillovers: Results from Two Large-Scale Experiments in Uganda"
Discussant: Molly Offer-Westort (Stanford)
[Video] [Paper] [Slides] [Discussant slides]
Tuesday, February 9, 2021: Martin Tingley and Jeffrey Wong (Netflix)
"Supporting Innovation and Scale with a Democratized Experimentation Platform"
Discussant: Iavor Bojinov (Harvard)
Tuesday, February 2, 2021: Interview with James Robins (Harvard)
[Video]
Tuesday, January 26, 2021: Stephen Bates (UC Berkeley)
"Causal Inference in Genetic Trio studies"
Discussant: Qingyuan Zhao (University of Cambridge)
[Video] [Slides] [Discussant slides] [Paper]
Tuesday, January 19, 2021: Mark van der Laan (UC Berkeley)
"Higher order Targeted Maximum Likelihood Estimation"
Discussant: Alex Luedtke (University of Washington)
[Video] [Slides] [Discussant slides]
Tuesday, January 12, 2021: Susan Athey (Stanford GSB)
"Synthetic Difference in Differences" (with Dmitry Arkhangelsky, David A. Hirshberg, Guido Imbens, Stefan Wager)
[Video] [Slides] [Paper]
Tuesday, December 15, 2020: Luke Miratrix (Harvard)
"Using national data and meta-analysis techniques to get a handle on how bad some biases might be in practice"
Discussant: Elizabeth Tipton (Northwestern University)
[Video] [Slides]
Tuesday, December 8, 2020: Qingyuan Zhao (University of Cambridge)
"Selection bias in 2020"
Discussant: Louisa Smith (Harvard University)
[Video] [Slides] [Discussant slides]
Tuesday, December 1, 2020: Vanessa Didelez (University of Bremen)
"Causal reasoning in survival and time-to-event analyses"
Discussant: Els Goetghebeur (Ghent University)
[Video] [Slides] [Paper 1] [Paper 2] [Paper 3] [Paper 4]
Tuesday, November 24, 2020: Fan Li (Yale)
"Propensity score weighting for covariate adjustment in randomized clinical trials"
Discussant: Kari Lock Morgan (Penn State University)
[Video] [Paper] [Slides] [Discussant slides]
Tuesday, November 17, 2020: Interview with Judea Pearl (UCLA)
[Video]
Tuesday, November 10, 2020: Dean Knox (Wharton) (with Justin Grimmer (Stanford) and Brandon Stewart (Princeton))
"Naïve regression requires weaker assumptions than factor models to adjust for multiple cause confounding"
Discussants: Betsy Ogburn (Johns Hopkins) (with Ilya Shpitser (Johns Hopkins) and Eric Tchetgen Tchetgen (Wharton))
[Video] [Paper] [Slides] [Discussant slides]
Tuesday, November 3, 2020: Interview with Donald Rubin (Harvard)
[Video]
Tuesday, October 27, 2020: David Blei (Columbia University)
"The Deconfounder: What is it? What is its theory? Is it useful?"
Discussant: Guido Imbens (Stanford)
[Video] [Paper] [Speaker slides] [Discussant slides]
Tuesday, October 20, 2020: Ismael Mourifie (University of Toronto)
"Testing Identification assumptions in Fuzzy Regression Discontinuity Designs"
Discussant: Zhuan Pei (Cornell)
[Video] [Paper (supplement)] [Speaker slides] [Discussant slides]
Monday, October 12, 2020: Interview with Esther Duflo (MIT)
[Video]
Tuesday, October 7, 2020: Peng Ding (UC Berkeley)
"Randomization and Regression Adjustment"
Discussant: Tirthankar DasGupta (Rutgers)
[Video] [Paper] [Speaker slides] [Discussant slides]
Tuesday, September 29, 2020: Emilija Perkovic (University of Washington)
"Causal effects in maximally oriented partially directed acyclic graphs (MPDAGs): Identification and efficient estimation"
Discussant: Thomas Richardson (University of Washington)
[Video] [Paper 1] [Paper 2] [Speaker slides] [Discussant slides]
Tuesday, September 23, 2020: Falco Bargagli Stoffi (Harvard); Eli Ben-Michael (UC Berkeley)
Talk 1: "Causal Rule Ensemble: Interpretable Inference of Heterogeneous Treatment Effects" (Falco Bargagli Stoffi)
Talk 2: "Synthetic Controls with Staggered Adoption" (Eli Ben-Michael)
[Video] [Bargagli Stoffi slides] [Ben-Michael slides]
Tuesday, September 15, 2020: Nathan Kallus and Xiaojie Mao (Cornell University)
"Localized Debiased Machine Learning: Efficient Inference on Quantile Treatment Effects and Beyond"
Discussant: Alexandre Belloni (Duke)
[Video] [Paper] [Speaker slides]
Tuesday, September 8, 2020: Joris Mooij (University of Amsterdam)
"Joint Causal Inference: A Unifying Perspective on Causal Discovery"
Discussant: Philip Dawid (University of Cambridge)
[Video] [Paper] [Speaker slides] [Discussant slides]
Tuesday, September 2, 2020: Karthika Mohan (Berkeley); David Hirshberg (Stanford)
Talk 1: "Causal Graphical Models for Handling Missing Data" (Karthika Mohan)
Talk 2: "Balance in Causal Inference: From Poststratification to Regularized Riesz Representers" (David Hirshberg)
[Hirshberg video] [Hirshberg slides]
Tuesday, August 25, 2020: Cynthia Rudin, Alexander Volfovsky, Sudeepa Roy (Duke)
"Almost Matching Exactly"
Discussant: Guillaume Basse (Stanford)
[Video] [Almost Matching Exactly Website] [Speaker slides] [Discussant slides]
Tuesday, August 18, 2020: Judith Lok (Boston University)
"Causal organic indirect and direct effects: closer to Baron and Kenny, with a product method for binary mediators"
Discussant: Kosuke Imai (Harvard University)
[Video] [Paper] [Speaker slides] [Discussant slides]
Tuesday, August 11, 2020: Panos Toulis (Chicago Booth)
"Randomization tests for spillovers under general interference: A graph-theoretic approach"
Discussant: Peng Ding (Berkeley)
[Video] [Paper] [Speaker slides] [Discussant slides]
Tuesday, August 4, 2020: Andrew Gelman (Columbia University)
"100 Stories of Causal Inference"
[Video] [Paper]
Tuesday, July 28, 2020: Georgia Papadogeorgou (University of Florida) and Lihua Lei (Stanford University)
Talk 1: "Causal inference with spatio-temporal data: estimating the effects of airstrikes on insurgent violence in Iraq" (Georgia Papadogeorgou)
Talk 2: "Conformal Inference of Counterfactuals and Individual Treatment Effects" (Lihua Lei)
[Video] [Papdogeorgou slides] [Lei slides] [Lei Paper] [Papadogeorgou Paper]
Tuesday, July 21, 2020: Yiqing Xu (Stanford) and Xun Pang (Tsinghua University)
"A Bayesian Alternative to Synthetic Control for Comparative Case Studies: A Dynamic Multilevel Latent Factor Model with Hierarchical Shrinkage"
Discussant: Dmitry Arkhangelsky (CEMFI, Madrid)
[Video] [Paper] [Speaker slides] [Discussant slides]
Tuesday, July 14, 2020: Michal Kolesár (Princeton)
"Finite-Sample Optimal Estimation and Inference on Average Treatment Effects Under Unconfoundedness"
Discussant: Luke Miratrix (Harvard)
[Video] [Paper] [Speaker slides] [Discussant Slides]
Tuesday, July 7, 2020: Caroline Uhler (MIT)
"Causal Inference in the Light of Drug Repurposing for SARS-CoV-2"
[Video] [Speaker slides]
Tuesday, June 30, 2020: Marloes Maathuis (ETH Zürich)
"Total causal effect estimation by combining causal structure learning and covariate adjustment"
Discussant: Daniel Malinsky (Columbia)
[Video] [Paper 1] [Paper 2] [Paper 3] [Paper 4 (supplement)] [Paper 5] [Speaker slides] [Discussant slides]
Tuesday, June 23, 2020: Eytan Bakshy (Facebook)
"Efficient Experimentation and Inference for Large Decision Spaces"
Discussant: Dean Eckles (MIT)
[Video] [Speaker slides] [Discussant Slides]
Tuesday, June 16, 2020: Jonas Peters (University of Copenhagen)
"Causality and distribution generalization"
Discussant: Yuansi Chen (ETH Zürich)
[Video] [Main paper] [Paper 1] [Paper 2] [Paper 3] [Speaker slides] [Discussant slides]
Tuesday, June 9, 2020: Dean Eckles (MIT)
"Noise induced randomization in regression discontinuity designs"
Discussant: Michal Kolesár (Princeton)
[Video] [Speaker slides]
Tuesday, June 2, 2020: Paul Rosenbaum (Wharton)
"Replication and Evidence Factors in Observational Studies"
[Video] [Handout] [Speaker slides]
Tuesday, May 26, 2020: Ya Xu (LinkedIn)
"Causal Inference Challenges in Industry: A perspective from experiences at LinkedIn"
Discussant: Iavor Bojinov (Harvard)
Abstract: In this talk, we will briefly give some background how online controlled experiments are commonly used in industry, and introduce some challenges we face, and also some opportunities in novel applications.
[Video] [Speaker slides] [Discussant slides]
Tuesday, May 19, 2020: Susan Murphy (Harvard University)
"Inference for Batched Bandits"
Discussant: Stefan Wager (Stanford University)
[Video] [Paper] [Speaker slides] [Discussant slides]
Tuesday, May 12, 2020: Ilya Shpitser (Johns Hopkins University)
"Identification and estimation in graphical models of missing data"
Discussant: Jin Tian (Iowa State University)
[Video] [Paper 1] [Paper 2] [Paper 3] [Speaker slides]
Tuesday, May 5, 2020: Eric Tchetgen Tchetgen (Wharton)
"Selective Machine Learning of Doubly Robust Functionals"
Discussant: Stijn Vansteelandt (UGent)
[Video] [Paper] [Speaker slides]
Tuesday, April 28, 2020: Edward Kennedy (Carnegie Mellon University)
"Optimal doubly robust estimation of heterogeneous causal effects"
Discussant: James Robins (Harvard University)
[Video] [Paper] [Speaker slides]
Tuesday, April 21, 2020: Elizabeth Ogburn (Johns Hopkins University)
"Social network dependence, unmeasured confounding, and the replication crisis"
Discussant: Ilya Shpitser (Johns Hopkins University)
[Video] [Paper] [Speaker slides]
Tuesday, April 14, 2020: Elizabeth Tipton (Northwestern University)
"Will this Intervention Work in this Population? Designing Randomized Trials for Generalization"
Discussant: Andrew Gelman (Columbia University)
[Video] [Website: The Generalizer] [Paper] [Speaker slides]
Tuesday, April 8, 2020: Hyunseung Kang (University of Wisconsin-Madison)
"Inferring Treatment Effects After Testing Instrument Strength in Linear Models" (w/ Nan Bi and Jonathan Taylor)
Discussant: Will Fithian (UC Berkeley)
[Video] [Paper] [Speaker slides]
Tuesday, March 31, 2020: Dylan Small (Wharton)
"Testing an Elaborate Theory of a Causal Hypothesis" (w/ Bikram Karmakar)
Discussant: Peter Bühlmann (ETH Zurich)
[Video] [Paper] [Speaker slides]