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Causal Inference: Post-treatment Bias and Conjoint Experiments
Fri, August 30, 8:00 to 9:30am, Marriott, Washington 3Session Submission Type: Created Panel
Session Description
All of these papers are about causal inference, some examining conjoint experiments, others post treatment bias.
Sub Unit
Individual Presentations
Design and Inference for Conjoint Analysis - Brandon de la Cuesta, Princeton University; Naoki Egami, Princeton University; Kosuke Imai, Harvard University
Contingent Conjoint Conclusions? How Information Format Shapes Cognition - Nicholas Winter, University of Virginia; Leah Malkovich, University of Virginia; Simonas Cepenas, University of Virginia
Informing Complier Average Treatment Effects with Post-treatment Variables - Shiyao Liu, Massachusetts Institute of Technology
Priming Bias versus Post-treatment Bias in Experimental Design - Matthew Blackwell, Harvard University; Jacob Brown, Harvard University; Sophie Hill, Harvard University; Kosuke Imai, Harvard University; Teppei Yamamoto, Massachusetts Institute of Technology