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Data-Adaptive Causal Effects and Superefficiency

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  • Aronow Peter M.

    (Departments of Political Science and Biostatistics, Yale University, 77 Prospect St., New Haven, CT 06520, USA)

Abstract

Recent approaches in causal inference have proposed estimating average causal effects that are local to some subpopulation, often for reasons of efficiency. These inferential targets are sometimes data-adaptive, in that they are dependent on the empirical distribution of the data. In this short note, we show that if researchers are willing to adapt the inferential target on the basis of efficiency, then extraordinary gains in precision can potentially be obtained. Specifically, when causal effects are heterogeneous, any asymptotically normal and root-n$n$ consistent estimator of the population average causal effect is superefficient for a data-adaptive local average causal effect.

Suggested Citation

  • Aronow Peter M., 2016. "Data-Adaptive Causal Effects and Superefficiency," Journal of Causal Inference, De Gruyter, vol. 4(2), pages 1-4, September.
    • Handle: RePEc:bpj:causin:v:4:y:2016:i:2:p:4:n:3
    DOI: 10.1515/jci-2016-0007
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    References listed on IDEAS

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    1. Richard K. Crump & V. Joseph Hotz & Guido W. Imbens & Oscar A. Mitnik, 2009. "Dealing with limited overlap in estimation of average treatment effects," Biometrika, Biometrika Trust, vol. 96(1), pages 187-199.
    2. Joshua D. Angrist & Jörn-Steffen Pischke, 2009. "Mostly Harmless Econometrics: An Empiricist's Companion," Economics Books, Princeton University Press, edition 1, number 8769.
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