1.1k citations · 1.5k across the 20 of their papers we have counts for
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Overlap in Observational Studies with High-Dimensional Covariates
Alexander D'Amour, Peng Ding, Avi Feller +2
Estimating causal effects under exogeneity hinges on two key assumptions: unconfoundedness and overlap. Researchers often argue that unconfoundedness is more plausible when more co…
Inference on a New Class of Sample Average Treatment Effects
Jasjeet S. Sekhon, Yotam Shem-Tov
We derive new variance formulas for inference on a general class of estimands of causal average treatment effects in a Randomized Control Trial (RCT). We generalize Robins (1988) a…
Meta-learners for Estimating Heterogeneous Treatment Effects using Machine Learning
Sören R. Künzel, Jasjeet S. Sekhon, Peter J. Bickel +1
There is growing interest in estimating and analyzing heterogeneous treatment effects in experimental and observational studies. We describe a number of meta-algorithms that can ta…
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…