Long Story Short: Omitted Variable Bias in Causal Machine Learning
为平均处理效应、因果导数等常见因果参数建立了遗漏变量偏误的一般理论，利用遗漏变量最大解释力的可信度判断来界定偏误，并提供了结合机器学习算法的统计推断方法，帮助研究者用简单工具对机器学习因果模型进行敏感性分析。
Abstract We develop a general theory of omitted variable bias for a wide range of common causal parameters, including average treatment effects, average causal derivatives, and policy effects from covariate shifts. We show how plausibility judgments on the maximum explanatory power of omitted variables are sufficient to bound the bias, facilitating sensitivity analysis in otherwise complex models. Finally, we provide statistical inference methods that can leverage modern machine learning algorithms for estimation. These results allow empirical researchers to perform sensitivity analyses in a flexible class of machine-learned causal models using very simple tools. Empirical examples demonstrate the utility of our approach.