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

翻译：干预措施的效果（即处理效应）的认知对于决策至关重要。使用条件平均处理效应（CATE）元学习器估计该处理效应的方法通常仅提供该处理效应的点估计，而额外的量化不确定性常被期望用于增强决策信心。为解决此问题，我们引入了两种新方法：保形卷积T学习器（CCT学习器）和保形蒙特卡洛（CMC）元学习器。这些方法利用加权保形预测系统（WCPS）、蒙特卡洛采样和CATE元学习器来生成个体处理效应（ITE）的预测分布，从而可能增强个体化决策。尽管我们展示了关于结果噪声分布的假设如何影响不确定性预测，但我们的实验表明，CCT和CMC元学习器在保持较窄区间宽度的同时实现了强覆盖性。它们还生成概率校准的预测分布，在各种合成和半合成数据集上提供了可靠的ITE范围。代码：https://github.com/predict-idlab/cct-cmc