Observational studies often present challenges for causal inference due to confounding and heterogeneity. In this paper, we illustrate how modern causal inference methods can be applied to large-scale academic salary data. Using records from 12,039 tenure-track faculty in the University of North Carolina system, linked with bibliometric indicators and institutional classifications, we estimate the causal effect of gender on faculty salaries. Our analysis combines propensity score matching with causal forests to adjust for rank, discipline, research productivity, and career experience. Results indicate that female faculty earn approximately 6% less than comparable male colleagues, with variation in the gap across career stages and levels of research productivity. This case study demonstrates how causal inference methods for observational data can provide insight into structural disparities in complex social systems.

翻译：观测性研究常因混杂因素和异质性而对因果推断构成挑战。本文阐述了如何将现代因果推断方法应用于大规模学术薪酬数据。基于北卡罗来纳大学系统12,039名终身制教职人员的记录，结合文献计量指标与机构分类，我们估计了性别对教职薪酬的因果效应。分析综合采用倾向得分匹配与因果森林方法，对职称、学科、研究产出与职业经历进行校正。结果表明，女性教职人员的薪酬比条件相当的男性同事低约6%，且差距在不同职业阶段与研究产出水平间存在差异。本案例研究展示了如何通过观测数据的因果推断方法揭示复杂社会系统中的结构性差异。