Causal inference of exact individual treatment outcomes in the presence of hidden confounders is rarely possible. Recent work has extended prediction intervals with finite-sample guarantees to partially identifiable causal outcomes, by means of a sensitivity model for hidden confounding. In deep learning, predictors can exploit their inductive biases for better generalization out of sample. We argue that the structure inherent to a deep ensemble should inform a tighter partial identification of the causal outcomes that they predict. We therefore introduce an approach termed Caus-Modens, for characterizing causal outcome intervals by modulated ensembles. We present a simple approach to partial identification using existing causal sensitivity models and show empirically that Caus-Modens gives tighter outcome intervals, as measured by the necessary interval size to achieve sufficient coverage. The last of our three diverse benchmarks 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，精确推断个体治疗结果的因果关系几乎不可能实现。近期研究通过引入针对隐藏混杂的敏感性模型，将具有有限样本保证的预测区间扩展至部分可识别的因果结果。在深度学习领域，预测器可利用其归纳偏好在样本外实现更好的泛化。我们认为深度集成固有的结构特征应能指导对其预测的因果结果进行更紧凑的部分识别。为此我们提出一种名为Caus-Modens的方法，通过调制集成来表征因果结果区间。我们提出了一种利用现有因果敏感性模型进行部分识别的简单方法，实验证明Caus-Modens能产生更紧凑的结果区间（以实现充分覆盖所需的最小区间大小为度量标准）。在三个多样化基准测试中，最后一个创新性地使用GPT-4进行观测实验，其中真实结果未知但可探测。