# 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 2022

**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: Tirthanker 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:

[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:

[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

[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:

[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:

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:

Talk 2:

[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:

Abstract:

[Video] [Speaker slides] [Discussant slides]**Tuesday, May 19, 2020: Susan Murphy (Harvard University)**"Inference for Batched Bandits"

Discussant:

[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:

[Video] [Paper] [Speaker slides]**Tuesday, April 28, 2020: Edward Kennedy (Carnegie Mellon University)**"Optimal doubly robust estimation of heterogeneous causal effects"

Discussant:

[Video] [Paper] [Speaker slides]**Tuesday, April 21, 2020: Elizabeth Ogburn (Johns Hopkins University)**"Social network dependence, unmeasured confounding, and the replication crisis"

Discussant:

[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:

[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]