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

Jun Wang, Wei Gao and Man‐Lai Tang

Scandinavian Journal of Statistics, 2019, vol. 46, issue 2, 575-594

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.

Date: 2019
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https://doi.org/10.1111/sjos.12363

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Persistent link: https://EconPapers.repec.org/RePEc:bla:scjsta:v:46:y:2019:i:2:p:575-594

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