All talks are listed in Pacific Time.

–
Coffee and Check-In
–
Opening Remarks
–
Causal Emergence: When Distortions In A Map Obscure The Territory
Frederick Eberhardt (Caltech)
–
Learning Causal Representations From Unknown Interventions
Kun Zhang (Carnegie Mellon University)
–
Break
–
Learning & Reasoning With Soft Interventions
Elias Bareinboim (Columbia University)
–
Online Reinforcement Learning With The Help Of Confounded Offline Data
Uri Shalit (Technion - Israel Institute of Technology)
–
Panel Discussion
Elias Bareinboim (Columbia University), Frederick Eberhardt (Caltech), Kun Zhang (Carnegie Mellon University), Uri Shalit (Technion - Israel Institute of Technology)
–
Lunch
–
Counterfactual Inference For Sequential Experiment Design
Raaz Dwivedi (MIT)
–
Break
–
Learning from RCTs in Public Health and Medicine
James Robins (Harvard University)
–
Panel Discussion
Raaz Dwivedi (MIT), James Robins (Harvard University)
–
Reception
–
Coffee and Check-In
–
To Adjust Or Not To Adjust? Estimating The Average Treatment Effect In Randomized Experiments With Missing Covariates
Peng Ding (UC Berkeley)
–
Searching For Causal Genetic Mechanisms Across Human Populations
Chiara Sabatti (Stanford University)
–
Break
–
Machine Learning-Based Design Of Proteins
Jennifer Listgarten (UC Berkeley)
–
Panel Discussion
Chiara Sabatti (Stanford University), Jennifer Listgarten (UC Berkeley), Peng Ding (UC Berkeley)
–
Lunch
–
Learning And Testing Causal Models: A Property Testing Viewpoint
Arnab Bhattacharyya (National University of Singapore, Singapore)
–
Some Staged Tree Models For Learning From Interventions
Liam Solus (KTH)
–
Break
–
Balancing Covariates In Randomized Experiments: The Gram--Schmidt Walk Design
Christopher Harshaw (Yale University)
–
Panel Discussion
Arnab Bhattacharyya (National University of Singapore, Singapore), Christopher Harshaw (Yale University), Liam Solus (KTH)
–
Coffee and Check-In
–
Learning Abstract Causal Structure From Causal Interventions In Children And Artificial Agents
Alison Gopnik (UC Berkeley)
–
Balancing Weights For Causal Effects With Panel Data: Some Recent Extensions To The Synthetic Control Method
Avi Feller (UC Berkeley)
–
Break
–
Designing Adaptive Experiments For Policy Learning And Inference
Molly Offer-Westort (University of Chicago)
–
Panel Discussion
Alison Gopnik (UC Berkeley), Avi Feller (UC Berkeley), Molly Offer-Westort (University of Chicago)
–
Lunch
–
Open Discussion (In-Person Only)
–
Coffee and Check-In
–
Causal Estimands And Interventions
Vanessa Didelez (Leibniz Institute, University of Bremen)
–
Unmeasured Spatial Confounding
Georgia Papadogeorgou (University of Florida)
–
Break
–
A Practitioner’S Guide To Intent-To-Treat Effects From Multisite (Blocked) Individually Randomized Trials: Estimands, Estimators, And Estimates
Luke Miratrix (Harvard University)
–
Panel Discussion
Georgia Papadogeorgou (University of Florida), Luke Miratrix (Harvard University), Vanessa Didelez (Leibniz Institute, University of Bremen)
–
Lunch
–
Causal Inference For Socio-Economic And Engineering Systems
Anish Agarwal (MIT)
–
Efficient Least Squares For Estimating Total Effects Under Linearity And Causal Sufficiency
Emilija Perkovic (University of Washington)
–
Break
–
A Robust, Differentially Private Randomized Experiment For Evaluating Online Educational Programs With Sensitive Student Data
Hyunseung Kang (University of Wisconsin, Madison)
–
Panel Discussion
Anish Agarwal (MIT), Emilija Perkovic (University of Washington), Hyunseung Kang (University of Wisconsin, Madison)