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Towards Practical Federated Causal Structure Learning

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Machine Learning and Knowledge Discovery in Databases: Research Track (ECML PKDD 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14170))

Abstract

Understanding causal relations is vital in scientific discovery. The process of causal structure learning involves identifying causal graphs from observational data to understand such relations. Usually, a central server performs this task, but sharing data with the server poses privacy risks. Federated learning can solve this problem, but existing solutions for federated causal structure learning make unrealistic assumptions about data and lack convergence guarantees. \(\textsc {FedC}^{2}\textsc {SL}\) is a federated constraint-based causal structure learning scheme that learns causal graphs using a federated conditional independence test, which examines conditional independence between two variables under a condition set without collecting raw data from clients. \(\textsc {FedC}^{2}\textsc {SL}\) requires weaker and more realistic assumptions about data and offers stronger resistance to data variability among clients. FedPC and FedFCI are the two variants of \(\textsc {FedC}^{2}\textsc {SL}\) for causal structure learning in causal sufficiency and causal insufficiency, respectively. The study evaluates \(\textsc {FedC}^{2}\textsc {SL}\) using both synthetic datasets and real-world data against existing solutions and finds it demonstrates encouraging performance and strong resilience to data heterogeneity among clients.

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Acknowledgement

We thank the anonymous reviewers for their insightful comments. We also thank Qi Pang for valuable discussions. This research is supported in part by the HKUST 30 for 30 research initiative scheme under the contract Z1283 and the Academic Hardware Grant from NVIDIA.

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Authors and Affiliations

  1. The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong SAR

    Zhaoyu Wang, Pingchuan Ma & Shuai Wang

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  1. Zhaoyu Wang
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  2. Pingchuan Ma
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  3. Shuai Wang
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Correspondence to Pingchuan Ma .

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Editors and Affiliations

  1. University of Michigan, Ann Arbor, MI, USA

    Danai Koutra

  2. University of Vienna, Vienna, Austria

    Claudia Plant

  3. Max Planck Institute for Software Systems, Kaiserslautern, Germany

    Manuel Gomez Rodriguez

  4. Politecnico di Torino, Turin, Italy

    Elena Baralis

  5. CENTAI, Turin, Italy

    Francesco Bonchi

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Wang, Z., Ma, P., Wang, S. (2023). Towards Practical Federated Causal Structure Learning. In: Koutra, D., Plant, C., Gomez Rodriguez, M., Baralis, E., Bonchi, F. (eds) Machine Learning and Knowledge Discovery in Databases: Research Track. ECML PKDD 2023. Lecture Notes in Computer Science(), vol 14170. Springer, Cham. https://doi.org/10.1007/978-3-031-43415-0_21

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  • DOI: https://doi.org/10.1007/978-3-031-43415-0_21

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