Invariant Graph Learning for Causal Effect Estimation
Author:
Affiliation:
1. University of Science and Technology of China, Hefei, China
2. Ant Group, Hangzhou, China
Funder
National Natural Science Foundation of China
Ant Group through CCF-Ant Research Fund
CCF- AFSG Research Fund
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3589334.3645549
Reference79 articles.
1. Invariance principle meets information bottleneck for out-of-distribution generalization;Ahuja Kartik;NeurIPS,2021
2. Ahmed M Alaa and Mihaela Van Der Schaar. 2017. Bayesian inference of individualized treatment effects using multi-task gaussian processes. NeurIPS, Vol. 30 (2017).
3. Martin Arjovsky, Léon Bottou, Ishaan Gulrajani, and David Lopez-Paz. 2019. Invariant risk minimization. arXiv preprint arXiv:1907.02893 (2019).
4. Shock-Based Causal Inference in Corporate Finance and Accounting Research
5. Analyzing Two-Stage Experiments in the Presence of Interference
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