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Enhancing Propensity Score Analysis with data Missing Not at Random: Introducing Dual-Forest Proximity Imputation

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  • Lee, Yongseok

    (University of Florida)

  • Leite, Walter

Abstract

Researchers using propensity score analysis (PSA) to estimate treatment effects using secondary data may have to handle data that is missing not at random (MNAR). Existing methods for PSA with MNAR data use logistic regression to model the missing data mechanisms, thus requiring manual specification of functional forms, and are difficult to implement with a large number of covariates. To overcome these limitations, this study proposes alternatives to existing methods by replacing logistic regression with a random forest. Also, it introduces the Dual-Forest Proximity imputation method, which leverages two types of proximity matrices of random forest techniques and incorporates missing pattern information in each matrix. Results from a Monte Carlo simulation show Dual-Forest Proximity imputation’s enhanced bias reduction with various types of MNAR mechanisms as compared to existing and alternative methods. A case study is also provided using data from the National Longitudinal Survey of Youth 1979 (NLSY79).

Suggested Citation

  • Lee, Yongseok & Leite, Walter, 2025. "Enhancing Propensity Score Analysis with data Missing Not at Random: Introducing Dual-Forest Proximity Imputation," OSF Preprints ex8ad_v1, Center for Open Science.
    • Handle: RePEc:osf:osfxxx:ex8ad_v1
    DOI: 10.31219/osf.io/ex8ad_v1
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    References listed on IDEAS

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    4. G. V. Kass, 1980. "An Exploratory Technique for Investigating Large Quantities of Categorical Data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 29(2), pages 119-127, June.
    5. Daniel McNeish, 2017. "Missing data methods for arbitrary missingness with small samples," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(1), pages 24-39, January.
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