Globally efficient non-parametric inference of average treatment effects by empirical balancing calibration weighting
The estimation of average treatment effects based on observational data is extremely important in practice and has been studied by generations of statisticians under different frameworks. Existing globally efficient estimators require non-parametric estimation of a propensity score function, an outc...
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Published in | Journal of the Royal Statistical Society. Series B, Statistical methodology Vol. 78; no. 3; pp. 673 - 700 |
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Main Authors | , , |
Format | Journal Article |
Language | English |
Published |
England
Blackwell Publishing Ltd
01.06.2016
John Wiley & Sons Ltd Oxford University Press |
Subjects | |
Online Access | Get full text |
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Summary: | The estimation of average treatment effects based on observational data is extremely important in practice and has been studied by generations of statisticians under different frameworks. Existing globally efficient estimators require non-parametric estimation of a propensity score function, an outcome regression function or both, but their performance can be poor in practical sample sizes. Without explicitly estimating either function, we consider a wide class of calibration weights constructed to attain an exact three-way balance of the moments of observed covariates among the treated, the control and the combined group. The wide class includes exponential tilting, empirical likelihood and generalized regression as important special cases, and extends survey calibration estimators to different statistical problems and with important distinctions. Global semiparametric efficiency for the estimation of average treatment effects is established for this general class of calibration estimators. The results show that efficiency can be achieved by solely balancing the covariate distributions without resorting to direct estimation of the propensity score or outcome regression function. We also propose a consistent estimator for the efficient asymptotic variance, which does not involve additional functional estimation of either the propensity score or the outcome regression functions. The variance estimator proposed outperforms existing estimators that require a direct approximation of the efficient influence function. |
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Bibliography: | 'Supplement to "Globally efficient nonparametric inference of average treatment effects by empirical balancing calibration weighting"'. istex:87C2C75691427BED177159BD7A20DFB52977FF24 University Grants Committee ArticleID:RSSB12129 Hong Kong Research Grants Council general research fund - No. 404012 Hong Kong Special Administrative Region US National Institutes of Health - No. R01HL122212 Chinese University of Hong Kong ark:/67375/WNG-M14FL1WH-0 ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
ISSN: | 1369-7412 1467-9868 |
DOI: | 10.1111/rssb.12129 |