Causal inference of exact individual treatment outcomes in the presence of hidden confounders is rarely possible. Instead, recent work has adapted conformal prediction to produce outcome intervals. Unfortunately this family of methods tends to be overly conservative, sometimes giving uninformative intervals. We introduce an alternative approach termed Caus-Modens, for characterizing causal outcome intervals by modulated ensembles. Motivated from Bayesian statistics and ensembled uncertainty quantification, Caus-Modens gives tighter outcome intervals in practice, measured by the necessary interval size to achieve sufficient coverage on three separate benchmarks. The last benchmark is a novel usage of GPT-4 for observational experiments with unknown but probeable ground truth.

翻译：在存在隐藏混杂因素的情况下，对个体治疗结果进行精确因果推断几乎不可能。因此，近期研究采用共形预测方法来生成结果区间。然而，这类方法往往过于保守，有时甚至会产生无信息区间。我们提出了一种替代方法，称为Caus-Modens，通过调制集成来表征因果结果区间。受贝叶斯统计和集成不确定性量化的启发，Caus-Modens在实践中能生成更紧的结果区间，这通过三个独立基准测试中实现充分覆盖率所需的最小区间大小得以衡量。最后一个基准测试创新性地应用GPT-4进行观测实验，其中真实结果未知但可探测。