The DOI matches its Crossref record; 129 of the 129 references it cites were found in a registry or an index.
A Survey on Causal Inference
Open accessAbstract
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.
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Cites
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D’ya Like DAGs? A Survey on Structure Learning and Causal Discovery
Vowels, M. J., Camgoz, N. C., & Bowden, R. (2023) · ACM Computing Surveys · doi:10.1145/3527154 · Cited by 237
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Causal identification of single-cell experimental perturbation effects with CINEMA-OT
Dong, M., Wang, B., Wei, J., de O. Fonseca, A. H., Perry, C. J., Frey, A., Ouerghi, F., Foxman, E. F., Ishizuka, J. J., Dhodapkar, R. M., et al. (2023) · Nature Methods · doi:10.1038/s41592-023-02040-5 · Cited by 94
Metadata
- Authors
- Yao, L., Chu, Z., Li, S., Li, Y., Gao, J., & Zhang, A.
- Venue
- ACM Transactions on Knowledge Discovery from Data
- Published
- 2021-05-10
- Volume
- 15
- Issue
- 5
- Pages
- 1-46
- ISSN
- 1556-4681 (ISSN-L)
- 1556-472X
- OpenAlex
- W3160537436
- Cited by
- 478
- References
- 129
- FWCI
- 52.50
- Type
- journal article
- Language
- en
- Open access
- bronze
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