In causal inference, parametric models are usually employed to address causal questions estimating the effect of interest. However, parametric models rely on the correct model specification assumption that, if not met, leads to biased effect estimates. Correct model specification is challenging, especially in high-dimensional settings. Incorporating Machine Learning (ML) into causal analyses may reduce the bias arising from model misspecification, since ML methods do not require the specification of a functional form of the relationship between variables. However, when ML predictions are directly plugged in a predefined formula of the effect of interest, there is the risk of introducing a “plug-in bias” in the effect measure. To overcome this problem and to achieve useful asymptotic properties, new estimators that combine the predictive potential of ML and the ability of traditional statistical methods to make inference about population parameters have been proposed. For epidemiologists interested in taking advantage of ML for causal inference investigations, we provide an overview of three estimators that represent the current state-of-art, namely Targeted Maximum Likelihood Estimation (TMLE), Augmented Inverse Probability Weighting (AIPW) and Double/Debiased Machine Learning (DML).

在因果推理中，参数模型通常用于解决估计兴趣影响的因果问题。但是，参数化模型依赖于正确的模型规范假设，如果不满足该假设，则会导致有偏差的效果估计。正确的模型规格具有挑战性，尤其是在高维设置中。将机器学习 （ML） 纳入因果分析可能会减少因模型错误指定而产生的偏差，因为 ML 方法不需要指定变量之间关系的函数形式。但是，当 ML 预测直接插入到感兴趣效果的预定义公式中时，存在在效果度量中引入“插件偏差”的风险。为了克服这个问题并获得有用的渐近特性，已经提出了新的估计器，它们结合了 ML 的预测潜力和传统统计方法对总体参数进行推断的能力。对于有兴趣利用 ML 进行因果推理调查的流行病学家，我们概述了代表当前最先进水平的三个估计器，即目标最大似然估计 （TMLE）、增强逆概率加权 （AIPW） 和双重/去偏机器学习 （DML）。