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Revisiting regression adjustment in experiments with heterogeneous treatment effects

Akanksha Negi and Jeffrey Wooldridge ()

Econometric Reviews, 2021, vol. 40, issue 5, 504-534

Abstract: In the context of random sampling, we show that linear full (separate) regression adjustment (FRA) on the control and treatment groups is, asymptotically, no less efficient than both the simple difference-in-means estimator and the pooled regression adjustment estimator; with heterogeneous treatment effects, FRA is usually strictly more efficient. We also propose a class of nonlinear regression adjustment estimators where consistency is ensured despite arbitrary misspecification of the conditional mean function. A simulation study confirms that nontrivial efficiency gains are possible with linear FRA, and that further gains are possible, even under severe mean misspecification, using nonlinear FRA.

Date: 2021
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DOI: 10.1080/07474938.2020.1824732

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