4 papers · 1 filter
Estimating Heterogeneous Causal Effects of High-Dimensional Treatments: Application to Conjoint Analysis
Max Goplerud, Kosuke Imai, Nicole E. Pashley
Estimation of heterogeneous treatment effects is an active area of research. Most of the existing methods, however, focus on estimating the conditional average treatment effects of…
Safe Policy Learning under Regression Discontinuity Designs with Multiple Cutoffs
Yi Zhang, Eli Ben-Michael, Kosuke Imai
The regression discontinuity (RD) design is widely used for program evaluation with observational data. The primary focus of the existing literature has been the estimation of the…
Priming bias versus post-treatment bias in experimental designs
Matthew Blackwell, Jacob R. Brown, Sophie Hill +2
Conditioning on variables affected by treatment can induce post-treatment bias when estimating causal effects. Although this suggests that researchers should measure potential mode…
Statistical Inference for Heterogeneous Treatment Effects Discovered by Generic Machine Learning in Randomized Experiments
Kosuke Imai, Michael Lingzhi Li
Researchers are increasingly turning to machine learning (ML) algorithms to investigate causal heterogeneity in randomized experiments. Despite their promise, ML algorithms may fai…