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

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

Angela Zhou (USC)
–
Lunch
–
Causal Inference in Cluster DAGs

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

Leonard Schulman (Caltech)
–
Statistical Inference from Networked Data

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

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

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

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

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

Bryon Aragam (University of Chicago)
–
Lunch
–
Open Problem Session
–
Break
–
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)
–
Augmented Balancing Weights as Linear Regression

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

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

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

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

Christopher Harshaw (UC Berkeley)
–
Break
–
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)
–
Discussion with Issa Kohler-Hausmann and Lily Hu