In recent decades, the fields of statistical and machine learning have seen a revolution in the development of data-adaptive regression methods that have optimal performance under flexible, sometimes minimal, assumptions on the true regression functions. These developments have impacted all areas of applied and theoretical statistics and have allowed data analysts to avoid the biases incurred under the pervasive practice of parametric model misspecification. In this commentary, I discuss issues around the use of data-adaptive regression in estimation of causal inference parameters. To ground ideas, I focus on two estimation approaches with roots in semi-parametric estimation theory: targeted minimum loss-based estimation (TMLE; van der Laan and Rubin, 2006) and double/debiased machine learning (DML; Chernozhukov and others, 2018). This commentary is not comprehensive, the literature on these topics is rich, and there are many subtleties and developments which I do not address. These two frameworks represent only a small fraction of an increasingly large number of methods for causal inference using machine learning. To my knowledge, they are the only methods grounded in statistical semi-parametric theory that also allow unrestricted use of data-adaptive regression techniques.

中文翻译：

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估计因果关系中的机器学习：针对性的基于最小损失的估计和双重/去偏机器学习。

在最近的几十年中，统计和机器学习领域见证了数据自适应回归方法的发展革命，该方法在对真实回归函数的灵活假设（有时甚至是最小假设）下具有最佳性能。这些发展影响了应用统计和理论统计的所有领域，并使数据分析员避免了因参数模型错误指定的普遍实践而引起的偏差。在这篇评论中，我讨论了在因果推断参数的估计中使用数据自适应回归的问题。扎根思想，我专注于两种基于半参数估计理论的估计方法：有针对性的基于最小损失的估计（TMLE; van der Laan和Rubin，2006）和双重/无偏机器学习（DML; Chernozhukov等，2018） ）。这篇评论并不全面，关于这些主题的文献很多，我没有谈到很多微妙之处和发展。这两个框架仅代表使用机器学习进行因果推理的越来越多方法中的一小部分。据我所知，它们是基于统计半参数理论的唯一方法，这些方法也允许无限制地使用数据自适应回归技术。