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
本文针对非结构化处理的因果推断问题，提出最大影响特征（MIF）的因果查询，开发估计算法与微调算法，在多类应用中验证其有效性。
AI 中文摘要
因果推断通常关注标量处理，但在许多问题中，处理是结构化的：文本、图像或临床决策序列。例如，讲师撰写课程描述以吸引更多学生，处理是课程描述，结果是注册人数。标准目标是将处理固定为一个精确值与另一个精确值的平均处理效应，存在两个问题：几乎没有完全相同的课程描述重复出现，无法找到可比组来测量效应，因此无法估计；即使能估计也几乎无用，因为没人希望所有课程都使用相同描述。讲师实际想知道的是描述的哪些特征能提高注册人数，以及这些特征中哪些可在多门课程中应用。为此，本文提出针对非结构化处理的因果查询：最大影响特征（MIF），即处理中对结果影响最大的特征。本文将MIF形式化为处理的二元特征，由特征评分函数定义，约束其两个值均有足够样本，以最大化其诱导的因果效应。开启该特征会使处理分布向显示该特征的方向偏移，关闭则相反，MIF效应对比两种平均潜在结果。本文研究MIF的识别条件，开发估计算法，并通过微调算法使其可操作，该算法沿MIF修改处理以生成改善结果的版本。本文在文本、图像和动态处理序列的应用中说明MIF算法的有效性。
英文摘要
Causal inference usually concerns a scalar treatment, yet in many problems the treatment is unstructured: a text, an image, or a sequence of clinical decisions. Consider an instructor writing a course description to attract more students: the treatment is the course description, and the outcome is enrollment. The standard target, the average treatment effect of fixing the treatment to one exact value versus another, runs into two problems. It cannot be estimated, because almost no exact description recurs across courses, leaving no comparable group from which to measure its effect; and it would be of little use even if it could, since no one wants every course to carry the same description. What the instructor actually wants to know is which features of a description raise enrollment, and which of those features can be acted on across many courses. To this end, we propose a causal query for unstructured treatments: the maximally influential feature (MIF), the feature of the treatment that most strongly influences the outcome. We formalize the MIF as a binary feature of the treatment, defined by a feature-scoring function, constrained so that both of its values stay well populated, and chosen to maximize the causal effect it induces. Turning the feature on shifts the distribution of treatments toward those that display it, turning it off shifts away, and the MIF effect contrasts the two average potential outcomes. We study identification conditions for the MIF, develop algorithms to estimate it, and make it actionable through a nudging algorithm that revises a treatment along the MIF into an outcome-improving version. We illustrate the MIF algorithm across applications in text, image, and dynamic treatment sequences.
Comments74 pages, 16 figures
