A Survey on Causal Inference

By Liuyi Yao, Zhixuan Chu, Sheng Li, Yaliang Li, Jing Gao, Aidong Zhang. ACM Transactions on Knowledge Discovery from Data, 2020

690 citations recorded by the source corpus.

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 frameworks. 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.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/f5236ce8add7920df5216ed3a6ffc5852666fd78

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Fei Sun
2. Yang Deng
3. Bolin Ding
4. Yaliang Li
5. Yuexiang Xie

In Statistics and Probability

1. Bolin Ding
2. Yaliang Li

Citations in the corpus

• Factual Consistency Evaluation for Text Summarization via Counterfactual Estimation (2021, methodology): “To be spe-cific, with the help of causal inference (Pearl and Mackenzie, 2018; Yao et al., 2021), we formulate the causal relationship among the source document, the generated summary, and the language prior to build up the causal graph for text summarization.”
• GraphLocator: Graph-Guided Causal Reasoning for Issue Localization (2025, unknown): “Traditional frameworks, such as structural causal models (SCMs)[27] and the potential outcome framework[10], provide systematic definitions and methods for discovering causal relationships [23, 33, 38] and estimating causal effects [42, 48].”