1.6k citations · 2.9k across the 36 of their papers we have counts for
16 papers · 1 filter
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…
Distributionally Robust Causal Inference with Observational Data
Dimitris Bertsimas, Kosuke Imai, Michael Lingzhi Li
We consider the estimation of average treatment effects in observational studies and propose a new framework of robust causal inference with unobserved confounders. Our approach is…
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…
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…
Using Machine Learning to Test Causal Hypotheses in Conjoint Analysis
Dae Woong Ham, Kosuke Imai, Lucas Janson
Conjoint analysis is a popular experimental design used to measure multidimensional preferences. Researchers examine how varying a factor of interest, while controlling for other r…
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…