We propose causal isotonic calibration, a novel nonparametric method for calibrating predictors of heterogeneous treatment effects. Furthermore, we introduce cross-calibration, a data-efficient variant of calibration that eliminates the need for hold-out calibration sets. Cross-calibration leverages cross-fitted predictors and generates a single calibrated predictor using all available data. Under weak conditions that do not assume monotonicity, we establish that both causal isotonic calibration and cross-calibration achieve fast doubly-robust calibration rates, as long as either the propensity score or outcome regression is estimated accurately in a suitable sense. The proposed causal isotonic calibrator can be wrapped around any black-box learning algorithm, providing robust and distribution-free calibration guarantees while preserving predictive performance.

翻译：我们提出因果等渗校准，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，我们引入交叉校准，这是一种数据高效的校准变体，无需使用保留校准集。交叉校准利用交叉拟合预测器，使用所有可用数据生成单一校准预测器。在弱条件（不假设单调性）下，我们证明只要倾向得分或结果回归在适当意义上得到准确估计，因果等渗校准与交叉校准均能达到快速双重稳健校准速率。所提出的因果等渗校准器可封装于任何黑箱学习算法之上，在保持预测性能的同时提供稳健且分布无关的校准保证。