Knowledge of the effect of interventions, called the treatment effect, is paramount for decision-making. Approaches to estimating this treatment effect, e.g. by using Conditional Average Treatment Effect (CATE) estimators, often only provide a point estimate of this treatment effect, while additional uncertainty quantification is frequently desired instead. Therefore, we present a novel method, the Conformal Monte Carlo (CMC) meta-learners, leveraging conformal predictive systems, Monte Carlo sampling, and CATE meta-learners, to instead produce a predictive distribution usable in individualized decision-making. Furthermore, we show how specific assumptions on the noise distribution of the outcome heavily affect these uncertainty predictions. Nonetheless, the CMC framework shows strong experimental coverage while retaining small interval widths to provide estimates of the true individual treatment effect.

利用条件平均治疗效果(CATE)估计器的一种新方法，结合符合性预测系统、蒙特卡罗采样和CATE元学习，以提供在个体化决策中使用的预测分布，并展示噪声分布的特定假设会对不确定性预测产生重大影响，然而，CMC框架在保持小间隔宽度的同时显示出强大的实验覆盖能力，以提供对真实个体治疗效果的估计。

预测个体治疗效果的共形蒙特卡洛元学习器