We propose causal isotonic calibration, a novel nonparametric method for calibrating predictors of heterogeneous treatment effects. In addition, we introduce a novel data-efficient variant of calibration that avoids the need for hold-out calibration sets, which we refer to as cross-calibration. Causal isotonic cross-calibration takes cross-fitted predictors and outputs a single calibrated predictor obtained using all available data. We establish under weak conditions that causal isotonic calibration and cross-calibration both achieve fast doubly-robust calibration rates so long as either the propensity score or outcome regression is estimated well in an appropriate sense. The proposed causal isotonic calibrator can be wrapped around any black-box learning algorithm to provide strong distribution-free calibration guarantees while preserving predictive performance.

翻译：我们提出因果保序校准，这是一种用于校准异质处理效应预测器的新型非参数方法。此外，我们引入了一种数据高效的新型校准变体，可避免对留出校准集的需求，并将其称为交叉校准。因果保序交叉校准接收交叉拟合后的预测器，并利用所有可用数据输出一个单一的已校准预测器。我们在弱条件下证明，只要倾向得分或结果回归在适当意义上得到良好估计，因果保序校准与交叉校准均能达到快速双重鲁棒校准速率。所提出的因果保序校准器可封装于任何黑箱学习算法之外，在保持预测性能的同时提供强概率分布无关的校准保证。