Conformal Convolution and Monte Carlo Meta-learners for Predictive Inference of Individual Treatment Effects
<|reference_start|>Conformal Convolution and Monte Carlo Meta-learners for Predictive Inference of Individual Treatment Effects: 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<|reference_end|>
@article{jonkers2024conformal,
    title={Conformal Convolution and Monte Carlo Meta-learners for Predictive
  Inference of Individual Treatment Effects},
    author={Jef Jonkers, Jarne Verhaeghe, Glenn Van Wallendael, Luc Duchateau,
  Sofie Van Hoecke},
    journal={arXiv preprint arXiv:2402.04906},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.04906},
    primaryClass={cs.LG stat.ML}
}