Semiparametric Estimation of Treatment Effects in Randomized Experiments

Susan Athey, Peter J. Bickel, Aiyou Chen, Guido Imbens and Michael Pollmann

No 29242, NBER Working Papers from National Bureau of Economic Research, Inc

Abstract: We develop new semiparametric methods for estimating treatment effects. We focus on a setting where the outcome distributions may be thick tailed, where treatment effects are small, where sample sizes are large and where assignment is completely random. This setting is of particular interest in recent experimentation in tech companies. We propose using parametric models for the treatment effects, as opposed to parametric models for the full outcome distributions. This leads to semiparametric models for the outcome distributions. We derive the semiparametric efficiency bound for this setting, and propose efficient estimators. In the case with a constant treatment effect one of the proposed estimators has an interesting interpretation as a weighted average of quantile treatment effects, with the weights proportional to (minus) the second derivative of the log of the density of the potential outcomes. Our analysis also results in an extension of Huber's model and trimmed mean to include asymmetry and a simplified condition on linear combinations of order statistics, which may be of independent interest.

JEL-codes: C01 C1 C14 (search for similar items in EconPapers)
Date: 2021-09
New Economics Papers: this item is included in nep-exp, nep-isf and nep-ore
Note: LS
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Citations: View citations in EconPapers (1)

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Related works:
Working Paper: Semiparametric Estimation of Treatment Effects in Randomized Experiments (2023)
Working Paper: Semiparametric Estimation of Treatment Effects in Randomized Experiments (2021)
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