Inference on a New Class of Sample Average Treatment Effects
Abstract
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) and show that when the estimand of interest is the Sample Average Treatment Effect of the Treated (SATT or SATC for controls), a consistent variance estimator exists. Although these estimands are equal to the Sample Average Treatment Effect (SATE) in expectation, potentially large differences in both accuracy and coverage can occur by the change of estimand, even asymptotically. Inference on the SATE, even using a conservative confidence interval, provides incorrect coverage of the SATT or SATC. We derive the variance and limiting distribution of a new and general class of estimands---any mixing between SATT and SATC---for which the SATE is a specific case. We demonstrate the applicability of the new theoretical results using Monte-Carlo simulations and an empirical application with hundreds of online experiments with an average sample size of approximately one hundred million observations per experiment. An R package, estCI, that implements all the proposed estimation procedures is available.
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Evidence Hub reads this as a randomized trial. Its evidence level is 2 of 5 (High · randomized trial). Randomized trials of a reasonable size are the strongest single-study design.
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