The Oaxaca-Blinder decomposition is a widely used method to explain social disparities. However, assigning causal meaning to its estimated components requires strong assumptions that often lack explicit justification. This article emphasizes the importance of clearly defined estimands and their identification when targeting mediating mechanisms of social disparities. Three approaches are distinguished based on their scientific questions and assumptions: a mediation approach and two interventional approaches. The Oaxaca-Blinder decomposition and Monte Carlo simulation-based g-computation are discussed for estimation in relation to these approaches. The latter method is used in an interventional effects analysis of the observed gender pay gap in Western Germany, using data from the 2017 German Socio-Economic Panel. Ten mediators, including indicators of human capital and job characteristics, are considered. Key findings indicate that the gender pay gap in log hourly wages could be reduced by up to 86% if these mediators were equally distributed between women and men. Substantial reductions could be achieved by aligning full-time employment and work experience.

翻译：Oaxaca-Blinder分解是解释社会差异的常用方法。然而，要为其估计分量赋予因果意义，需要依赖通常缺乏明确论证的强假设。本文强调在针对社会差异的中介机制时，明确定义估计量及其识别条件的重要性。根据科学问题和假设条件，本文区分了三种研究路径：中介效应分析法和两种干预效应分析法。文中讨论了Oaxaca-Blinder分解和基于蒙特卡洛模拟的g-计算法在这三种路径中的估计应用。后者被应用于西德性别薪酬差距的干预效应分析，数据来源于2017年德国社会经济追踪调查。研究考虑了包括人力资本指标和工作特征在内的十个中介变量。关键结果表明：若这些中介变量在男女间均衡分布，对数小时工资的性别差距最多可缩减86%。其中，全日制就业和工作经验的均衡分布能实现最大程度的差距缩减。