Algorithms are commonly used to predict outcomes under a particular decision or intervention, such as predicting whether an offender will succeed on parole if placed under minimal supervision. Generally, to learn such counterfactual prediction models from observational data on historical decisions and corresponding outcomes, one must measure all factors that jointly affect the outcomes and the decision taken. Motivated by decision support applications, we study the counterfactual prediction task in the setting where all relevant factors are captured in the historical data, but it is either undesirable or impermissible to use some such factors in the prediction model. We refer to this setting as runtime confounding. We propose a doubly-robust procedure for learning counterfactual prediction models in this setting. Our theoretical analysis and experimental results suggest that our method often outperforms competing approaches. We also present a validation procedure for evaluating the performance of counterfactual prediction methods.

翻译：一般情况下,为了从历史决定和相应结果的观察数据中学习反事实预测模型,我们必须测量共同影响结果和决定的所有因素。我们受决定支持应用程序的驱动,在历史数据中记录所有相关因素的环境下研究反事实预测任务,但在预测模型中使用某些此类因素是不可取的或不允许的。我们把这一环境称为运行时间混乱。我们建议采用双曲线程序来学习这一环境中的反事实预测模型。我们的理论分析和实验结果表明,我们的方法往往优于相互竞争的方法。我们还提出了一个用于评价反事实预测方法的绩效的验证程序。