Causal isotonic calibration for heterogeneous treatment effects

• URL: http://arxiv.org/abs/2302.14011v2
• Date: Tue, 6 Jun 2023 01:33:44 GMT
• Title: Causal isotonic calibration for heterogeneous treatment effects
• Authors: Lars van der Laan, Ernesto Ulloa-P\'erez, Marco Carone, and Alex Luedtke
• Abstract summary: We propose causal isotonic calibration, a novel nonparametric method for calibrating predictors of heterogeneous treatment effects. We also introduce cross-calibration, a data-efficient variant of calibration that eliminates the need for hold-out calibration sets.
• Score: 0.5249805590164901
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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