Design-based theory for causal inference

TL;DR

This paper reviews recent advances in design-based causal inference, emphasizing randomized experiments, covariate balance, and extensions to complex data settings like high-dimensional data and network interference.

Contribution

It provides a systematic review of recent theoretical and methodological progress in design-based causal inference, highlighting new design strategies and inference methods for complex scenarios.

Findings

Advances in covariate-balanced randomization designs

Development of design-based inference methods for high-dimensional data

Extensions to noncompliance and network interference scenarios

Abstract

Causal inference, as a major research area in statistics and data science, plays a central role across diverse fields such as medicine, economics, education, and the social sciences. Design-based causal inference begins with randomized experiments and emphasizes conducting statistical inference by leveraging the known randomization mechanism, thereby enabling identification and estimation of causal effects under weak model dependence. Grounded in the seminal works of Fisher and Neyman, this paradigm has evolved to include various design strategies, such as stratified randomization and rerandomization, and analytical methods including Fisher randomization tests, Neyman-style asymptotic inference, and regression adjustment. In recent years, with the emergence of complex settings involving high-dimensional data, individual noncompliance, and network interference, design-based causal inference has witnessed remarkable theoretical and methodological advances. This paper provides a systematic review of recent progress in this field, focusing on covariate-balanced randomization designs, design-based statistical inference methods, and their extensions to high-dimensional, noncompliance, and network interference scenarios. It concludes with a comprehensive perspective on future directions for the theoretical development and practical applications of causal inference.