Papers › Causal isotonic calibration for heterogeneous treatment effects

Causal isotonic calibration for heterogeneous treatment effects

27 Feb 2023arXiv:2302.14011archive 2025-07-28

Lars van der Laan, Ernesto Ulloa-Pérez, Marco Carone, Alex Luedtke

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.

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