Experimental Design For Causal Inference Through An Optimization Lens

TL;DR

This paper explores how viewing experimental design as an optimization problem can improve the efficiency of causal inference across various fields, providing a flexible framework for designing cost-effective experiments.

Contribution

It introduces a novel perspective that frames experimental design for causal inference as an optimization problem, enabling the use of optimization tools to enhance experiment efficiency.

Findings

Framework for optimization-based experimental design

Potential for cost reduction in experiments

Enhanced flexibility in designing causal studies

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.