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Nonparametric causal inference with functional covariates

Author

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  • Kurisu, Daisuke
  • Otsu, Taisuke
  • Xu, Mengshan

Abstract

Functional data and their analysis have become increasingly popular in various fields of data science. This article considers estimation and inference of the average treatment effect under unconfoundedness when the covariates involve a functional variable, and proposes the inverse probability weighting estimator, where the propensity score is estimated by using a kernel estimator for functional variables. We establish the √-consistency and asymptotic normality of the proposed estimator. Numerical experiments and an empirical application demonstrate the usefulness of the proposed method.

Suggested Citation

  • Kurisu, Daisuke & Otsu, Taisuke & Xu, Mengshan, 2025. "Nonparametric causal inference with functional covariates," LSE Research Online Documents on Economics 127990, London School of Economics and Political Science, LSE Library.
    • Handle: RePEc:ehl:lserod:127990
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    File URL: http://eprints.lse.ac.uk/127990/
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    JEL classification:

    • J1 - Labor and Demographic Economics - - Demographic Economics

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