17 citations · 33 across the 4 of their papers we have counts for
4 papers
Empirical Bayes Double Shrinkage for Combining Biased and Unbiased Causal Estimates
Evan T. R. Rosenman, Francesca Dominici, Luke Miratrix
Motivated by the proliferation of observational datasets and the need to integrate non-randomized evidence with randomized controlled trials, causal inference researchers have rece…
Randomization Inference for Treatment Effect Variation
Peng Ding, Avi Feller, Luke Miratrix
Applied researchers are increasingly interested in whether and how treatment effects vary in randomized evaluations, especially variation not explained by observed covariates. We p…
To Adjust or Not to Adjust? Sensitivity Analysis of M-Bias and Butterfly-Bias
Peng Ding, Luke Miratrix
"M-Bias," as it is called in the epidemiologic literature, is the bias introduced by conditioning on a pretreatment covariate due to a particular "M-Structure" between two latent f…
Concise comparative summaries (CCS) of large text corpora with a human experiment
Jinzhu Jia, Luke Miratrix, Bin Yu +4
In this paper we propose a general framework for topic-specific summarization of large text corpora and illustrate how it can be used for the analysis of news databases. Our framew…