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Invited Paper Session

Novel Causal Inference Methods for Analyzing Modern Experimental Data in Health and Social Sciences

Tue, Aug 5, 8:30 AM - 10:20 AM Room CC-103C Music City Center

Siyu HengOrganizerSiyu HengChair

WNAR co: Health Policy Statistics Sectionco: Social Statistics Section Applied

4 Presentations

Causal inference in network experiments: regression-based analysis and design-based properties

8:35 AM - 9:00 AM

Mengsi Gao (UC Berkeley)

Co-authors: Peng Ding (University of California-Berkeley)

Covariate adjustment exposure mapping interference model misspecification network- robust standard error weighted least squares

Statistical performance guarantee for selecting those predicted to benefit most from treatment

9:00 AM - 9:25 AM

Michael Lingzhi Li (Harvard Business School)

causal inference exceptional responders heterogeneous treatment effects uniform confidence bands

Improving instrumental variable estimators with post-stratification, with applications to experiments studying get-out-the-vote (GOTV) efforts

9:25 AM - 9:50 AM

Nicole Pashley (Rutgers University)

Co-authors: Luke Miratrix (Harvard University), Luke Keele (University of Pennsylvania)

Causal inference Post-stratification Instrumental variables Blocking Compliance Randomization Inference

Evaluating tax audit policies on large interfirm networks : a field experiment

9:50 AM - 10:15 AM

Panagiotis Toulis (The University of Chicago, Booth School of Business)

randomization spillovers tax policies interfirm networks big data

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