1.1k citations · 1.5k across the 20 of their papers we have counts for
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Nonparametric identification is not enough, but randomized controlled trials are
P. M. Aronow, James M. Robins, Theo Saarinen +2
We argue that randomized controlled trials (RCTs) are special even among settings where average treatment effects are identified by a nonparametric unconfoundedness assumption. Thi…
Linear Aggregation in Tree-based Estimators
Sören R. Künzel, Theo F. Saarinen, Edward W. Liu +1
Regression trees and their ensemble methods are popular methods for nonparametric regression: they combine strong predictive performance with interpretable estimators. To improve t…
Shrinkage Estimators in Online Experiments
Drew Dimmery, Eytan Bakshy, Jasjeet Sekhon
We develop and analyze empirical Bayes Stein-type estimators for use in the estimation of causal effects in large-scale online experiments. While online experiments are generally t…
Causaltoolbox---Estimator Stability for Heterogeneous Treatment Effects
Sören R. Künzel, Simon J. S. Walter, Jasjeet S. Sekhon
Estimating heterogeneous treatment effects has become increasingly important in many fields and life and death decisions are now based on these estimates: for example, selecting a…
Worth Weighting? How to Think About and Use Weights in Survey Experiments
Luke W. Miratrix, Jasjeet S. Sekhon, Alexander G. Theodoridis +1
The popularity of online surveys has increased the prominence of using weights that capture units' probabilities of inclusion for claims of representativeness. Yet, much uncertaint…
Generalized full matching and extrapolation of the results from a large-scale voter mobilization experiment
Fredrik Sävje, Michael J. Higgins, Jasjeet S. Sekhon
Matching is an important tool in causal inference. The method provides a conceptually straightforward way to make groups of units comparable on observed characteristics. The use of…
Blocking estimators and inference under the Neyman-Rubin model
Michael J. Higgins, Fredrik Sävje, Jasjeet S. Sekhon
We derive the variances of estimators for sample average treatment effects under the Neyman-Rubin potential outcomes model for arbitrary blocking assignments and an arbitrary numbe…