Scalable Differentiable Causal Discovery in the Presence of Latent Confounders with Skeleton Posterior-Reference-Cited by-同舟云学术

Scalable Differentiable Causal Discovery in the Presence of Latent Confounders with Skeleton Posterior

Published:2024-08-24 Issue: Volume:34 Page:2141-2152
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Container-title:Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
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Author:

Ma Pingchuan¹^ORCID,Ding Rui²^ORCID,Fu Qiang²^ORCID,Zhang Jiaru³^ORCID,Wang Shuai¹^ORCID,Han Shi²^ORCID,Zhang Dongmei²^ORCID

Affiliation:

1. HKUST, Hong Kong, Hong Kong

2. Microsoft Research, Beijing, China

3. Shanghai Jiao Tong University, Shanghai, China

Funder

RGC CRF

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3637528.3672031

Reference46 articles.

1. Raj Agrawal, Caroline Uhler, and Tamara Broderick. 2018. Minimal I-MAP MCMC for scalable structure discovery in causal DAG models. In International Conference on Machine Learning. PMLR, 89--98.

2. Luca Ambrogioni, Umut Gücclü, Julia Berezutskaya, Eva Borne, Ya?mur Gücclütürk, Max Hinne, Eric Maris, and Marcel Gerven. 2019. Forward amortized inference for likelihood-free variational marginalization. In The 22nd International Conference on Artificial Intelligence and Statistics. PMLR, 777--786.

3. Matthew Ashman, Chao Ma, Agrin Hilmkil, Joel Jennings, and Cheng Zhang. 2022. Causal Reasoning in the Presence of Latent Confounders via Neural ADMG Learning. In The Eleventh International Conference on Learning Representations.

4. Daniel Bernstein, Basil Saeed, Chandler Squires, and Caroline Uhler. 2020. Ordering-based causal structure learning in the presence of latent variables. In International Conference on Artificial Intelligence and Statistics. PMLR, 4098--4108.

5. Rohit Bhattacharya, Tushar Nagarajan, Daniel Malinsky, and Ilya Shpitser. 2021. Differentiable causal discovery under unmeasured confounding. In International Conference on Artificial Intelligence and Statistics. PMLR, 2314--2322.