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• W3005312434 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." @default.
• W3005312434 created "2020-02-14" @default.
• W3005312434 creator A5008967163 @default.
• W3005312434 creator A5013588572 @default.
• W3005312434 creator A5026512168 @default.
• W3005312434 creator A5032616889 @default.
• W3005312434 creator A5046576694 @default.
• W3005312434 creator A5089176198 @default.
• W3005312434 date "2020-02-05" @default.
• W3005312434 modified "2023-09-26" @default.
• W3005312434 title "A Survey on Causal Inference" @default.
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