Past Talks and Recordings

The list of past talks (by quarters) with titles, speakers, discussants, and relevant links. Click the subpages to view the same list with abstracts. 

Fall 2024

• Tuesday, October 15, 2024: Oliver Dukes (Ghent University)
  - Title: Nonparametric tests of treatment effect homogeneity for policy-makers
  - Discussant:  Edward Kennedy (Carnegie Mellon University)
  - Abstract: Recent work has focused on nonparametric estimation of conditional treatment effects, but inference has remained relatively unexplored. We propose a class of nonparametric tests for both quantitative and qualitative treatment effect heterogeneity. The tests can incorporate a variety of structured assumptions on the conditional average treatment effect, allow for both continuous and discrete covariates, and do not require sample splitting. Furthermore, we show how the tests are tailored to detect alternatives where the population impact of adopting a personalized decision rule differs from using a rule that discards covariates. The proposal is thus relevant for guiding treatment policies. The utility of the proposal is borne out in simulation studies and a re-analysis of an AIDS clinical trial. This is joint work with Mats Stensrud, Riccardo Brioschi and Aaron Hudson.
  [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
  - Abstract: As causal ground truth is incredibly rare, causal discovery algorithms are commonly only evaluated on simulated data. This is concerning, given that simulations reflect preconceptions about generating processes regarding noise distributions, model classes, and more. In this talk, I present a method for falsifying the output of a causal discovery algorithm in the absence of ground truth. The key insight is that while statistical learning seeks stability across subsets of data points, causal learning should seek stability across subsets of variables. Motivated by this insight, our method relies on a notion of compatibility between causal graphs learned on different subsets of variables. Detecting incompatibilities can falsify wrongly inferred causal relations due to violation of assumptions or errors from finite sample effects. Although passing such compatibility tests is only a necessary criterion for good performance, I will argue that it provides strong evidence for the causal models whenever compatibility entails strong implications for the joint distribution.
  [Paper][Video][Slides]
  Speaker 2: Bijan Mazaheri (Broad Institute of MIT and Harvard)
  - Title: Synthetic Potential Outcomes and the Hierarchy of Causal Identifiability
  - Abstract: A mixture model consists of a latent class that exerts a discrete signal on the observed data. Uncovering these latent classes is fundamental to unsupervised learning and forms the backbone of scientific thought. In this talk, we consider the problem of recovering latent classes of causal responses to an intervention. We allow overlapping support in the distributions of these classes, meaning individuals cannot be clustered into groups with a similar response. Instead, we develop a method of moments approach to synthetically sample potential outcome distributions using the higher-order multi-linear moments of the observable data. This approach is the first known identifiability result for what we call Mixtures of Treatment Effects (MTEs). More broadly, we show how MTEs fit into a hierarchy of mixture identifiability that unifies a number of previous approaches to latent class confounding.
  [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)
  - Abstract: We consider a setting where there are N heterogeneous units and p interventions. Our goal is to learn unit-specific potential outcomes for any combination of these p interventions, i.e., N×2^p causal parameters. Choosing a combination of interventions is a problem that naturally arises in a variety of applications such as factorial design experiments and recommendation engines (e.g., showing a set of movies that maximizes engagement for a given user). Running N×2^p experiments to estimate the various parameters is likely expensive and/or infeasible as N and p grow. Further, with observational data there is likely confounding, i.e., whether or not a unit is seen under a combination is correlated with its potential outcome under that combination. We study this problem under a novel model that imposes latent structure across both units and combinations of interventions. Specifically, we assume latent similarity in potential outcomes across units (i.e., the matrix of potential outcomes is approximately rank r) and regularity in how combinations of interventions interact (i.e., the coefficients in the Fourier expansion of the potential outcomes is approximately s sparse). We establish identification for all N×2^p parameters despite unobserved confounding. We propose an estimation procedure, Synthetic Combinations, and establish finite-sample consistency under precise conditions on the observation pattern. We show that Synthetic Combinations is able to consistently estimate unit-specific potential outcomes given a total of poly(r)×(N+s^{2}p) observations. In comparison, previous methods that do not exploit structure across both units and combinations have poorer sample complexity scaling as min(N×s^{2}p, r×(N+2^p)).
  [Paper][Video] [Slides] [Discussion slides]

Spring 2024

• 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]

Winter 2024

• 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] 

Fall 2023

• 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]

Spring 2023

• 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]

Winter 2023

• 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] 

Fall 2022

• 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]

Spring 2022

• 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]

Winter 2022

• 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]

Fall 2021

• 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]

Summer 2021

• 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]

Spring 2021

• 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]

Winter 2021

• 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]

Fall 2020

• 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]

Summer 2020

• 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]

Spring 2020

• 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]