个体治疗效果（ITE）预测是机器学习的重要研究领域，其目的在解释和估算粒状水平时的作用的因果影响。它代表了对诸如医疗保健，在线广告或社会经济学的多个申请兴趣的问题。为了促进本主题的研究，我们释放了从几个随机控制试验中收集的1390万个样本的公开收集，通过健康的210倍因素扩展先前可用的数据集。我们提供有关数据收集的详细信息，并执行Sanity检查以验证使用此数据是否有因果推理任务。首先，我们正规化可以使用此数据执行的隆起建模（UM）的任务以及相关的评估指标。然后，我们提出了为ITE预测提供了一般设置的合成响应表面和异质处理分配。最后，我们报告实验以验证利用其大小的数据集的关键特性，以评估和比较 - 具有高统计显着性 - 基线UM和ITE预测方法的选择。

Counterfactual reasoning from logged data has become increasingly important for many applications such as web advertising or healthcare. In this paper, we address the problem of learning stochastic policies with continuous actions from the viewpoint of counterfactual risk minimization (CRM). While the CRM framework is appealing and well studied for discrete actions, the continuous action case raises new challenges about modelization, optimization, and~offline model selection with real data which turns out to be particularly challenging. Our paper contributes to these three aspects of the CRM estimation pipeline. First, we introduce a modelling strategy based on a joint kernel embedding of contexts and actions, which overcomes the shortcomings of previous discretization approaches. Second, we empirically show that the optimization aspect of counterfactual learning is important, and we demonstrate the benefits of proximal point algorithms and differentiable estimators. Finally, we propose an evaluation protocol for offline policies in real-world logged systems, which is challenging since policies cannot be replayed on test data, and we release a new large-scale dataset along with multiple synthetic, yet realistic, evaluation setups.