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Targeted smoothing parameter selection for estimating average causal effects
Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics. (Stat4Reg)
Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics. (Stat4Reg)
2014 (English)In: Computational statistics (Zeitschrift), ISSN 0943-4062, E-ISSN 1613-9658, Vol. 29, no 6, 1727-1748 p.Article in journal (Refereed) Published
Abstract [en]

The non-parametric estimation of average causal effects in observational studies often relies on controlling for confounding covariates through smoothing regression methods such as kernel, splines or local polynomial regression. Such regression methods are tuned via smoothing parameters which regulates the amount of degrees of freedom used in the fit. In this paper we propose data-driven methods for selecting smoothing parameters when the targeted parameter is an average causal effect. For this purpose, we propose to estimate the exact expression of the mean squared error of the estimators. Asymptotic approximations indicate that the smoothing parameters minimizing this mean squared error converges to zero faster than the optimal smoothing parameter for the estimation of the regression functions. In a simulation study we show that the proposed data-driven methods for selecting the smoothing parameters yield lower empirical mean squared error than other methods available such as, e.g., cross-validation.

Place, publisher, year, edition, pages
Springer, 2014. Vol. 29, no 6, 1727-1748 p.
Keyword [en]
causal inference, double smoothing, local linear regression
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:umu:diva-108232DOI: 10.1007/s00180-014-0515-0ISI: 000345099200017OAI: oai:DiVA.org:umu-108232DiVA: diva2:851616
Available from: 2015-09-07 Created: 2015-09-07 Last updated: 2017-12-04Bibliographically approved

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Häggström, Jennyde Luna, Xavier

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