复杂原因的操作化：调解的实用主义观点
Operationalizing Complex Causes: A Pragmatic View of Mediation
摘要：
我们研究了复杂对象的因果响应估计问题（例如，文本、图像、基因组学）。在这种情况下，传统的\emph{atomic}干预通常不可用（例如，改变字符、像素、DNA碱基对）。相反，我们只能获得间接或\emph{crude}干预（例如，参加写作计划、修改场景、应用基因疗法）。在这项工作中，我们正式这个问题，并提供了一个初步的解决方案。鉴于候选调解人的集合，我们提出（a）一个两步法预测的因果关系的反应，原油干预措施;和（b）一个测试程序，以确定调解人的原油干预措施。我们证明，在一系列模拟和现实世界的启发的例子，我们的方法使我们能够有效地估计从新的治疗方案的有限数据的原油干预的效果。
英文摘要：
We examine the problem of causal response estimation for complex objects (e.g., text, images, genomics). In this setting, classical \emph{atomic} interventions are often not available (e.g., changes to characters, pixels, DNA base-pairs). Instead, we only have access to indirect or \emph{crude} interventions (e.g., enrolling in a writing program, modifying a scene, applying a gene therapy). In this work, we formalize this problem and provide an initial solution. Given a collection of candidate mediators, we propose (a) a two-step method for predicting the causal responses of crude interventions; and (b) a testing procedure to identify mediators of crude interventions. We demonstrate, on a range of simulated and real-world-inspired examples, that our approach allows us to efficiently estimate the effect of crude interventions with limited data from new treatment regimes.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
