发表机构
University of Michigan（密歇根大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文针对非结构化结果提出因果查询，通过学习最大对比特征（MCF）解决传统平均处理效应不适用于非结构化结果的问题，经文本、图像实证验证算法可恢复处理改变的结果显著方面。
AI 中文摘要
传统因果推断聚焦于标量结果，例如患者是否康复、工人的收入、网站的访问量等。而现代研究越来越多地提出关于更丰富形式结果的因果问题，如临床记录、开放式调查回复和图像。医院可能想知道AI文档工具如何改变医生撰写的记录，或护士培训项目如何改变患者在调查回复中所说的内容。对于此类结果，通常的平均处理效应定义不明确：无法有意义地将一段文本或一张图像从另一段中减去。为此，本文提出了针对非结构化结果的因果查询，核心思路是学习结果中受处理影响最大的特征，将其称为最大对比特征（MCF）。为估计MCF，本文学习一个特征评分函数，该函数将每个结果映射为标量，并揭示处理组与对照组潜在结果之间最显著的对比。本文为该查询建立了识别条件和估计算法，并通过允许特征评分函数依赖于观测协变量，将其扩展到异质效应的情况，还处理了处理和结果均为非结构化的场景。在文本和图像上的实证研究表明，该算法能恢复结果中被处理改变的显著方面。
英文摘要
Causal inference has traditionally centered on scalar outcomes: whether a patient recovers, how much a worker earns, or how many visits a website receives. Modern studies increasingly ask causal questions about outcomes with richer form, such as clinical notes, open-ended survey responses, and images. A hospital may want to know how an AI documentation tool changes the notes physicians write, or how a nurse training program alters what patients say in survey responses. For such outcomes, the usual average treatment effect is ill-defined: one cannot meaningfully subtract one text or image from another. To this end, we propose a causal query for unstructured outcomes. The key idea is to learn what features of the outcome are most causally affected by the treatment, which we call the maximally contrasting feature (MCF). To estimate the MCF, we learn a feature-scoring function that maps each outcome to a scalar and exposes the sharpest contrast between treated and control potential outcomes. We develop identification conditions and estimation algorithms for this query, and extend it to heterogeneous effects by allowing the feature-scoring function to depend on observed covariates. We also handle settings where both the treatment and the outcome are unstructured. Empirical studies on text and images show that the algorithm recovers salient aspects of an outcome changed by a treatment.
Comments68 pages, 10 figures
