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Coffee and Check-in
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Opening Remarks
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A Cautious Approach To Constraint-Based Causal Model Selection Based on Equivalence Tests

Daniel Malinsky (Columbia University)
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Break
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Empirical Gateaux Derivatives for Causal Inference and Optimization-Based Estimators

Angela Zhou (USC)
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Lunch
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Causal Inference in Cluster DAGs

Jin Tian (Iowa State University)
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Break
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Identifiability of Statistical Models with Latent Symmetries

Leonard Schulman (Caltech)
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Statistical Inference from Networked Data

Constantinos Daskalakis (Massachusetts Institute of Technology)
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Reception
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Coffee and Check-in
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Why DAGs are Inappropriate for Causal Reasoning in Complex Systems

Johannes Textor (Radboud University Nijmegen, The Netherlands)
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Break
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Generalizing Conditional Independence: Nested Markov Models

Thomas Richardson (University of Washington)
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Lunch
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Stable Discovery of Interpretable Subgroups via Calibration in Causal Studies

Bin Yu (UC Berkeley)
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Break
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Causality & LLMs Discussion
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Coffee and Check-in
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Confounding and Dependence in Spatial Statistics

Betsy Ogburn (John Hopkins)
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Break
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Optimal Neighbourhood Selection in Structural Equation Models

Bryon Aragam (University of Chicago)
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Lunch
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Open Problem Session
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Break
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Target Trials and Structural Nested Models: Emulating RCTs using Observational Longitudinal Data: Recent Theoretical Empirical Results and Increasing Role in Clinical Medicine

James Robins (Harvard University)
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Augmented Balancing Weights as Linear Regression

David Bruns-Smith (UC Berkeley)
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Coffee and Check-in
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Causal Discovery Under Limited Global Confounding

Bijan Mazaheri (Caltech)
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Break
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Active Causal Structure Learning with Advice

Arnab Bhattacharyya (National University of Singapore)
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On Measuring Causal Contributions via Do-Interventions

Shiva Kasiviswanathan (Amazon)
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Lunch
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A New Design-Based Framework for Randomized Experiments and the Riesz Estimator

Christopher Harshaw (UC Berkeley)
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Break
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Discussion with Issa Kohler-Hausmann and Lily Hu
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Neglected Assumptions in Causal Inference and Their Implications for the Quantitative Study of Bias and Discrimination

Issa Kohler-Hausmann (Yale Law School),
Lily Hu (Yale University),
Betsy Ogburn (John Hopkins)