提出可解释的因果特征，让文本图像等复杂处理更有效
Causal Inference with Unstructured Treatments
- 定义最大影响力特征（MIF），找出最影响结果的处理特征
- 通过开关特征对比，量化其对结果的因果效应
- 适用于教育、医疗等需优化非结构化决策的场景
传统因果推断针对标量处理，但许多问题中处理是无结构的：如文本、图像或临床决策序列。例如课程描述影响招生人数，标准平均处理效应无法估计，因几乎无完全重复的描述；且即使可估计也无实际意义，因不希望所有课程描述相同。真正需求是识别哪些描述特征能提升招生，并可在多课程中应用。为此，我们提出针对无结构处理的因果查询：最大影响力特征（MIF），即对结果影响最强的处理特征。我们将MIF形式化为一个二值特征，由特征评分函数定义，要求两个取值均保持充分分布，且最大化其所诱导的因果效应。开启特征使处理分布偏向包含该特征，关闭则偏离，MIF效应对比两者平均潜在结果。我们研究了MIF的可识别性条件，开发了估计算法，并通过一种提示算法实现可操作性，将原处理沿MIF方向调整为结果改善版本。在文本、图像和动态处理序列中验证了该方法的有效性。
原文摘要 · Abstract (English)
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.
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