Semiparametric Estimation of Treatment Effects in Randomized Experiments

Susan Athey, Peter J. Bickel, Aiyou Chen, Guido Imbens and Michael Pollmann
Additional contact information
Peter J. Bickel: University of California, Berkeley
Aiyou Chen: Google LLC
Michael Pollmann: Stanford University

Research Papers from Stanford University, Graduate School of Business

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.

Date: 2021-09
New Economics Papers: this item is included in nep-exp
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Citations: View citations in EconPapers (1)

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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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Persistent link: https://EconPapers.repec.org/RePEc:ecl:stabus:3986

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