A Survey on Causal Inference

TL;DR

This survey comprehensively reviews causal inference methods under the potential outcome framework, highlighting traditional and machine learning approaches, their applications, datasets, and tools for researchers and practitioners.

Contribution

It provides a detailed comparison of causal inference methods, including traditional and machine learning techniques, within the potential outcome framework, and summarizes datasets and open-source tools.

Findings

Comparison of traditional and machine learning methods

Applications in advertising, medicine, and recommendation systems

Summary of benchmark datasets and open-source codes

Abstract

Causal inference is a critical research topic across many domains, such as statistics, computer science, education, public policy and economics, for decades. Nowadays, estimating causal effect from observational data has become an appealing research direction owing to the large amount of available data and low budget requirement, compared with randomized controlled trials. Embraced with the rapidly developed machine learning area, various causal effect estimation methods for observational data have sprung up. In this survey, we provide a comprehensive review of causal inference methods under the potential outcome framework, one of the well known causal inference framework. The methods are divided into two categories depending on whether they require all three assumptions of the potential outcome framework or not. For each category, both the traditional statistical methods and the recent machine learning enhanced methods are discussed and compared. The plausible applications of these methods are also presented, including the applications in advertising, recommendation, medicine and so on. Moreover, the commonly used benchmark datasets as well as the open-source codes are also summarized, which facilitate researchers and practitioners to explore, evaluate and apply the causal inference methods.