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A Robustness Index of Propensity Score Estimation to Uncontrolled Confounders

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Statistical Causal Inferences and Their Applications in Public Health Research

Part of the book series: ICSA Book Series in Statistics ((ICSABSS))

Abstract

As a crucial component of propensity score methods for reducing selection bias, propensity score estimation can only account for observed covariates. The behaviors of sensitivity and robustness of propensity score estimation to the impact of unobserved covariates or uncontrolled confounders have not been fully understood. This chapter introduces a new technique to assess the sensitivity and robustness of propensity score estimation to the impact of uncontrolled confounders. The sensitivity is defined as a change from a propensity score that is estimated from a propensity score model including all observed covariates to a potential propensity score that would be estimated from the propensity score model adding an unobserved covariate. The robustness is subsequently defined as the probability of the sensitivity would cross a pre-specified threshold. To assess the robustness, a reference distribution of the sensitivity is derived by borrowing information from observed covariates and further approximated to one of Pearson distributions. This procedure of assessment is illustrated with empirical data on substance abuse prevention for high-risk youth.

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Author information

Authors and Affiliations

  1. Duke University School of Nursing, 307 Trent Dr., DUMC Box 3322, Durham, NC, 27710, USA

    Wei Pan

  2. Department of Educational & Human Sciences, University of Central Florida, 161250, Orlando, FL, 32816, USA

    Haiyan Bai

Authors
  1. Wei Pan
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  2. Haiyan Bai
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Correspondence to Wei Pan .

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Editors and Affiliations

  1. Department of Epidemiology School of Public Health and Tropical Medicine, Tulane University, New Orleans, Louisiana, USA

    Hua He

  2. Christiana Care Health System, Value Institute, Newark, Delaware, USA

    Pan Wu

  3. School of Social Work and Department of Biostatistics, University of North Carolina, Chapel Hill, North Carolina, USA

    Ding-Geng (Din) Chen

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Pan, W., Bai, H. (2016). A Robustness Index of Propensity Score Estimation to Uncontrolled Confounders. In: He, H., Wu, P., Chen, DG. (eds) Statistical Causal Inferences and Their Applications in Public Health Research. ICSA Book Series in Statistics. Springer, Cham. https://doi.org/10.1007/978-3-319-41259-7_4

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