发表机构
Massachusetts Institute of Technology（麻省理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出混合学习视角，将潜在混杂视为异质性来源，通过恢复混合分布与分量机制，实现干预分布和因果估计量的估计，并连接高维混合学习与因果推断。
AI 中文摘要
未观测混杂是观测数据因果推断中的一个基本挑战。本文发展了一种混合学习视角，将潜在混杂因子视为异质性的来源，这种异质性在观测数据中诱导出混合结构。在合适的结构和可识别性假设下，恢复混合分布和分量机制能够实现对干预分布和因果估计量的估计。以伯努利混合的变体作为贯穿性示例，我们将混合学习技术及其结构假设置于具体情境中，并将其与面板数据环境下的因果推断联系起来，包括潜在因子模型和合成对照方法。随后，我们考虑具有相依结果轨迹的高维指数族混合，将研究从反事实均值推进到对反事实分布的建模。我们将这一视角与处理未观测混杂的其他互补方法进行对比定位。综合来看，这些思想在混合学习与因果推断之间架起了一座桥梁，将高维混合学习的最新进展与因果效应的可扩展识别和估计联系起来，同时也为混合学习提出了新的挑战。
英文摘要
Unobserved confounding is a fundamental challenge in causal inference from observational data. This article develops a mixture-learning perspective, viewing latent confounders as sources of heterogeneity that induce mixture structure in observed data. Under suitable structural and identifiability assumptions, recovering the mixing distribution and component mechanisms enables estimation of interventional distributions and causal estimands. Using variants of Bernoulli mixtures as a running example, we contextualize mixture-learning techniques and their structural assumptions, and connect them to causal inference in panel-data settings, including latent factor models and synthetic interventions.We then consider high-dimensional exponential-family mixtures with dependent outcome trajectories, moving beyond counterfactual means to model counterfactual distributions. We situate this perspective relative to complementary approaches for unobserved confounding. Together, these ideas provide a bridge between mixture learning and causal inference, connecting recent advances in high-dimensional mixture learning to scalable identification and estimation of causal effects while raising new challenges for mixture learning.
