Semiparametric Estimation of Treatment Effects in Randomized Experiments

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

Papers from arXiv.org

Abstract: We develop new semiparametric methods for estimating treatment effects. We focus on settings where the outcome distributions may be thick tailed, where treatment effects may be small, where sample sizes are large and where assignment is completely random. This setting is of particular interest in recent online experimentation. We propose using parametric models for the treatment effects, leading to semiparametric models for the outcome distributions. We derive the semiparametric efficiency bound for the treatment effects for this setting, and propose efficient estimators. In the leading case with constant quantile treatment effects one of the proposed efficient 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 suggests an extension of Huber's model and trimmed mean to include asymmetry.

Date: 2021-09, Revised 2023-08
New Economics Papers: this item is included in nep-ecm, nep-exp and nep-isf
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Citations: View citations in EconPapers (3)

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http://arxiv.org/pdf/2109.02603 Latest version (application/pdf)

Related works:
Working Paper: Semiparametric Estimation of Treatment Effects in Randomized Experiments (2021)
Working Paper: Semiparametric Estimation of Treatment Effects in Randomized Experiments (2021)
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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2109.02603

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