Recent developments in causal machine learning methods have made it easier to estimate flexible relationships between confounders, treatments and outcomes, making unconfoundedness assumptions in causal analysis more palatable. How successful are these approaches in recovering ground truth baselines? In this paper we analyze a new data sample including an experimental rollout of a new feature at a large technology company and a simultaneous sample of users who endogenously opted into the feature. We find that recovering ground truth causal effects is feasible -- but only with careful modeling choices. Our results build on the observational causal literature beginning with LaLonde (1986), offering best practices for more credible treatment effect estimation in modern, high-dimensional datasets.

翻译：因果机器学习方法的最新进展使得更容易估计混杂因素、处理变量与结果之间的灵活关系，从而使因果分析中的无混杂假设更具可行性。这些方法在恢复真实基线方面有多成功？在本文中，我们分析了一个新的数据样本，其中包括一家大型科技公司新功能的实验性发布，以及同时期内生选择使用该功能的用户样本。我们发现，恢复真实因果效应是可行的——但需要谨慎的建模选择。我们的研究结果建立在自LaLonde（1986）以来的观察性因果文献基础上，为现代高维数据集中更具可信度的处理效应估计提供了最佳实践。