标题： 平衡得分的概率分析用于因果推断 显示英文标题

标题： Probabilistic Analysis of Balancing Scores for Causal Inference

摘要： 倾向得分常用于观察性数据中处理组和对照组的分层，以在所谓的潜在结果因果建模框架中估计处理对结果的因果效应时消除混杂偏差。 在本文中，我们试图从概率角度了解倾向得分的基本行为。 我们对它们的使用进行了简单的分析，仅限于离散的混杂协变量和结果的情况。 在明确倾向得分行为的同时，我们的分析展示了如何同时推导出所谓的预后得分。 然而，目前文献中预后得分的推导是有限的，而我们的推导更为一般，并展示了拥有该得分的所有可能性。 我们称之为结果得分。 我们认为，应用倾向得分和结果得分是减少混杂协变量维度最有效的方法，这与目前认为倾向得分 alone 是最有效方法的观点相反。 显示英文摘要

摘要： Propensity scores are often used for stratification of treatment and control groups of subjects in observational data to remove confounding bias when estimating of causal effect of the treatment on an outcome in so-called potential outcome causal modeling framework. In this article, we try to get some insights into basic behavior of the propensity scores in a probabilistic sense. We do a simple analysis of their usage confining to the case of discrete confounding covariates and outcomes. While making clear about behavior of the propensity score our analysis shows how the so-called prognostic score can be derived simultaneously. However the prognostic score is derived in a limited sense in the current literature whereas our derivation is more general and shows all possibilities of having the score. And we call it outcome score. We argue that application of both the propensity score and the outcome score is the most efficient way for reduction of dimension in the confounding covariates as opposed to current belief that the propensity score alone is the most efficient way.