Experimental Design For Causal Inference Through An Optimization Lens

Jinglong Zhao
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Abstract

The study of experimental design offers tremendous benefits for answering causal questions across a wide range of applications, including agricultural experiments, clinical trials, industrial experiments, social experiments, and digital experiments. Although valuable in such applications, the costs of experiments often drive experimenters to seek more efficient designs. Recently, experimenters have started to examine such efficiency questions from an optimization perspective, as experimental design problems are fundamentally decision-making problems. This perspective offers a lot of flexibility in leveraging various existing optimization tools to study experimental design problems. This manuscript thus aims to examine the foundations of experimental design problems in the context of causal inference as viewed through an optimization lens.
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通过优化视角进行因果推理的实验设计
对实验设计的研究为回答农业实验、临床试验、工业实验、社会实验和数字实验等广泛应用中的因果问题提供了巨大的益处。尽管在这些应用中很有价值,但实验成本往往促使实验人员寻求更高效的设计。最近,实验人员开始从优化的角度研究此类效率问题,因为实验设计问题从根本上说是决策问题。这一视角为利用各种现有优化工具研究实验设计问题提供了很大的灵活性。因此,本手稿旨在通过优化视角,研究因果推断背景下的实验设计问题的基础。
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