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Estimation of treatment effects for heterogeneous matched‐pairs data with probit models

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  • Jun Wang
  • Wei Gao
  • Man‐Lai Tang

Abstract

Estimating the effect of medical treatments on subject responses is one of the crucial problems in medical research. Matched‐pairs designs are commonly implemented in the field of medical research to eliminate confounding and improve efficiency. In this article, new estimators of treatment effects for heterogeneous matched‐pairs data are proposed. Asymptotic properties of the proposed estimators are derived. Simulation studies show that the proposed estimators have some advantages over the famous Heckman's estimator, the conditional maximum likelihood estimator, and the inverse probability weighted estimator. We apply the proposed methodology to a data set from a study of low‐birth‐weight infants.

Suggested Citation

  • Jun Wang & Wei Gao & Man‐Lai Tang, 2019. "Estimation of treatment effects for heterogeneous matched‐pairs data with probit models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 46(2), pages 575-594, June.
    • Handle: RePEc:bla:scjsta:v:46:y:2019:i:2:p:575-594
    DOI: 10.1111/sjos.12363
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