Matching in Selective and Balanced Representation Space for Treatment Effects Estimation

@article{Chu2020MatchingIS,
  title={Matching in Selective and Balanced Representation Space for Treatment Effects Estimation},
  author={Zhixuan Chu and Stephen L. Rathbun and Sheng Li},
  journal={Proceedings of the 29th ACM International Conference on Information \& Knowledge Management},
  year={2020}
}
  • Zhixuan Chu, S. Rathbun, Sheng Li
  • Published 15 September 2020
  • Computer Science
  • Proceedings of the 29th ACM International Conference on Information & Knowledge Management
The dramatically growing availability of observational data is being witnessed in various domains of science and technology, which facilitates the study of causal inference. However, estimating treatment effects from observational data is faced with two major challenges, missing counterfactual outcomes and treatment selection bias. Matching methods are among the mostly widely used and fundamental approaches to estimating treatment effects, but existing matching methods have poor performance… 

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