Conformal Convolution and Monte Carlo Meta-learners for Predictive Inference of Individual Treatment Effects

• URL: http://arxiv.org/abs/2402.04906v4
• Date: Wed, 12 Jun 2024 12:35:14 GMT
• Title: Conformal Convolution and Monte Carlo Meta-learners for Predictive Inference of Individual Treatment Effects
• Authors: Jef Jonkers, Jarne Verhaeghe, Glenn Van Wallendael, Luc Duchateau, Sofie Van Hoecke,
• Abstract summary: Conformal convolution T-learner (CCT-learner) and conformal Monte Carlo (CMC) meta-learners are presented. The approaches leverage weighted conformal predictive systems (WCPS), Monte Carlo sampling, and CATE meta-learners. They generate predictive distributions of individual treatment effect (ITE) that could enhance individualized decision-making.
• Score: 2.7320409129940684
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Knowledge of the effect of interventions, known as the treatment effect, is paramount for decision-making. Approaches to estimating this treatment effect using conditional average treatment effect (CATE) meta-learners often provide only a point estimate of this treatment effect, while additional uncertainty quantification is frequently desired to enhance decision-making confidence. To address this, we introduce two novel approaches: the conformal convolution T-learner (CCT-learner) and conformal Monte Carlo (CMC) meta-learners. The approaches leverage weighted conformal predictive systems (WCPS), Monte Carlo sampling, and CATE meta-learners to generate predictive distributions of individual treatment effect (ITE) that could enhance individualized decision-making. Although we show how assumptions about the noise distribution of the outcome influence the uncertainty predictions, our experiments demonstrate that the CCT- and CMC meta-learners achieve strong coverage while maintaining narrow interval widths. They also generate probabilistically calibrated predictive distributions, providing reliable ranges of ITEs across various synthetic and semi-synthetic datasets. Code: https://github.com/predict-idlab/cct-cmc

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