As predictive models -- e.g., from machine learning -- give likely outcomes, they may be used to reason on the effect of an intervention, a causal-inference task. The increasing complexity of health data has opened the door to a plethora of models, but also the Pandora box of model selection: which of these models yield the most valid causal estimates? Here we highlight that classic machine-learning model selection does not select the best outcome models for causal inference. Indeed, causal model selection should control both outcome errors for each individual, treated or not treated, whereas only one outcome is observed. Theoretically, simple risks used in machine learning do not control causal effects when treated and non-treated population differ too much. More elaborate risks build proxies of the causal error using ``nuisance'' re-weighting to compute it on the observed data. But does computing these nuisance adds noise to model selection? Drawing from an extensive empirical study, we outline a good causal model-selection procedure: using the so-called $R\text{-risk}$; using flexible estimators to compute the nuisance models on the train set; and splitting out 10\% of the data to compute risks.

翻译：随着预测模型——例如来自机器学习的模型——能够给出可能的结果，它们可用于推理干预效应（一项因果推断任务）。健康数据日益复杂的特性为大量模型打开了大门，但也开启了模型选择的潘多拉魔盒：这些模型中哪些能产生最有效的因果估计？在此我们强调，经典的机器学习模型选择并不能选出最适合因果推断的结果模型。事实上，因果模型选择应控制每个个体（无论是否接受处理）的结果误差，而实际只观测到其中一个结果。从理论上讲，当处理组和非处理组人群差异过大时，机器学习中使用的简单风险无法控制因果效应。更精细的风险利用“干扰项”重新加权构建因果误差的代理变量，以便在观测数据上计算它。但计算这些干扰项是否会给模型选择引入噪声？基于一项广泛的实证研究，我们概述了一个良好的因果模型选择流程：使用所谓的 $R\text{-风险}$；在训练集上使用灵活估计器计算干扰模型；并划分出10%的数据用于计算风险。