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arXiv · 2208.08065

Revisiting the propensity score's central role: Towards bridging balance and efficiency in the era of causal machine learning

Nima S. Hejazi·Mark J. van der Laan
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Abstract

About forty years ago, in a now--seminal contribution, Rosenbaum & Rubin (1983) introduced a critical characterization of the propensity score as a central quantity for drawing causal inferences in observational study settings. In the decades since, much progress has been made across several research fronts in causal inference, notably including the re-weighting and matching paradigms. Focusing on the former and specifically on its intersection with machine learning and semiparametric efficiency theory, we re-examine the role of the propensity score in modern methodological developments. As Rosenbaum & Rubin (1983)'s contribution spurred a focus on the balancing property of the propensity score, we re-examine the degree to which and how this property plays a role in the development of asymptotically efficient estimators of causal effects; moreover, we discuss a connection between the balancing property and efficient estimation in the form of score equations and propose a score test for evaluating whether an estimator achieves balance.

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Nima S. Hejazi, Mark J. van der Laan. 2022-08-17. Revisiting the propensity score's central role: Towards bridging balance and efficiency in the era of causal machine learning. https://doi.org/10.1353/obs.2023.0001

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