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.

翻译：我们提出了一种新颖方法——一致蒙特卡洛（CMC）元学习器，该方法结合了一致预测系统、蒙特卡洛采样和CATE元学习器，可生成用于个性化决策的预测分布。知识背景：决策制定中理解干预效应（称为治疗效应）至关重要。现有估算治疗效应的方法（如使用条件平均治疗效应（CATE）估计器）往往仅提供点估计，而实际应用中常需额外的不确定性量化。我们进一步展示了结果噪声分布的具体假设如何显著影响这些不确定性预测。尽管如此，CMC框架在保持较小区间宽度的同时展现了强大的实验覆盖率，从而能够准确估计真实的个体治疗效应。