The estimation of causal effects with observational data continues to be a
very active research area. In recent years, researchers have developed new
frameworks which use machine learning to relax classical assumptions necessary
for the estimation of causal effects. In this paper, we review one of the most
prominent methods - "double/debiased machine learning" (DML) - and empirically
evaluate it by comparing its performance on simulated data relative to more
traditional statistical methods, before applying it to real-world data. Our
findings indicate that the application of a suitably flexible machine learning
algorithm within DML improves the adjustment for various nonlinear confounding
relationships. This advantage enables a departure from traditional functional
form assumptions typically necessary in causal effect estimation. However, we
demonstrate that the method continues to critically depend on standard
assumptions about causal structure and identification. When estimating the
effects of air pollution on housing prices in our application, we find that DML
estimates are consistently larger than estimates of less flexible methods. From
our overall results, we provide actionable recommendations for specific choices
researchers must make when applying DML in practice.

通过对模拟数据和真实世界数据的性能比较，本文评估了一种名为 “双重 / 无偏机器学习”（DML）的方法，在估算因果效应时，使用机器学习放宽了传统假设的限制。我们发现，将灵活的机器学习算法应用于 DML 可以提高对各种非线性混淆关系的调整，从而避免了传统因果效应估计中通常需要的功能形式假设。然而，我们证明这种方法仍然严重依赖于因果结构和识别的标准假设。在我们的应用中，估计空气污染对房价的影响时，我们发现 DML 估计结果一直比不太灵活的方法估计结果要大。基于我们的整体结果，我们为研究人员在实际应用 DML 时提供了可行的建议。