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Propensity score modelling in observational studies using dimension reduction methods

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  • Ghosh, Debashis

Abstract

Conditional independence assumptions are very important in causal inference modelling as well as in dimension reduction methodologies. These are two very strikingly different statistical literatures, and we study links between the two in this article. The concept of covariate sufficiency plays an important role, and we provide theoretical justification when dimension reduction and partial least squares methods will allow for valid causal inference to be performed. The methods are illustrated with application to a medical study and to simulated data.

Suggested Citation

  • Ghosh, Debashis, 2011. "Propensity score modelling in observational studies using dimension reduction methods," Statistics & Probability Letters, Elsevier, vol. 81(7), pages 813-820, July.
    • Handle: RePEc:eee:stapro:v:81:y:2011:i:7:p:813-820
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    References listed on IDEAS

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    Cited by:

    1. Cho, Youngjoo & Zhan, Xiang & Ghosh, Debashis, 2022. "Nonlinear predictive directions in clinical trials," Computational Statistics & Data Analysis, Elsevier, vol. 174(C).
    2. Wei Luo & Yeying Zhu & Debashis Ghosh, 2017. "On estimating regression-based causal effects using sufficient dimension reduction," Biometrika, Biometrika Trust, vol. 104(1), pages 51-65.
    3. Wei Luo, 2022. "On efficient dimension reduction with respect to the interaction between two response variables," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(2), pages 269-294, April.
    4. Joseph Antonelli & Matthew Cefalu & Nathan Palmer & Denis Agniel, 2018. "Doubly robust matching estimators for high dimensional confounding adjustment," Biometrics, The International Biometric Society, vol. 74(4), pages 1171-1179, December.

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