Decomposing Treatment Effect Variation
By Peng Ding, Avi Feller, Luke W. Miratrix. Journal of the American Statistical Association, 2016
98 citations recorded by the source corpus.
ABSTRACT Understanding and characterizing treatment effect variation in randomized experiments has become essential for going beyond the “black box” of the average treatment effect. Nonetheless, traditional statistical approaches often ignore or assume away such variation. In the context of randomized experiments, this article proposes a framework for decomposing overall treatment effect variation into a systematic component explained by observed covariates and a remaining idiosyncratic component. Our framework is fully randomization-based, with estimates of treatment effect variation that are entirely justified by the randomization itself. Our framework can also account for noncompliance, which is an important practical complication. We make several contributions. First, we show that randomization-based estimates of systematic variation are very similar in form to estimates from fully interacted linear regression and two-stage least squares. Second, we use these estimators to develop an omnibus test for systematic treatment effect variation, both with and without noncompliance. Third, we propose an R2-like measure of treatment effect variation explained by covariates and, when applicable, noncompliance. Finally, we assess these methods via simulation studies and apply them to the Head Start Impact Study, a large-scale randomized experiment. Supplementary materials for this article are available online.
Subfields: Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/c2357d428ede5d746c765ea39b3797945f51e9ee
How researchers see this paper
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In Statistics and Probability
- Stefan Wager
- Robert Tibshirani
- Jonathan E. Taylor
- Peng Ding
- Xinran Li
- Susan Athey
- E.J. Candes
- Wenfei Du
Citations in the corpus
- High-dimensional regression adjustments in randomized experiments (2016, methodology): “This line of research has been thoroughly explored: under low-dimensional asymptotics where the problem specification remains fixed while the number of samples grows to infinity, it is now well-established that regression adjustments are always asymptotically helpful—even in misspecified models—prov”
- Rerandomization and regression adjustment (2019, background): “In general, these confidence intervals can be conservative, and can be improved (Aronow et al. 2014; Fogarty 2018; Ding et al. 2019).”