ION-C: Integration of Overlapping Networks via Constraints
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Abstract (arXiv)
In many causal learning problems, variables of interest are often not all measured over the same observations, but are instead distributed across multiple datasets with overlapping variables. Tillman et al. (2008) presented the first algorithm for enumerating the minimal equivalence class of ground-truth DAGs consistent with all input graphs by exploiting local independence relations, called ION. In this paper, this problem is formulated as a more computationally efficient answer set programming (ASP) problem, which we call ION-C, and solved with the ASP system clingo. The ION-C algorithm was run on random synthetic graphs with varying sizes, densities, and degrees of overlap between subgraphs, with overlap having the largest impact on runtime, number of solution graphs, and agreement within the output set. To validate ION-C on real-world data, we ran the algorithm on overlapping graphs learned from data from two successive iterations of the European Social Survey (ESS), using a procedure for conducting joint independence tests to prevent inconsistencies in the input.
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References · 8
- 2019Advances and Open Problems in Federated Learningcited by 3,046
- 2017Multi-shot ASP solving with clingocited by 115
- 2023Causal-learn: Causal Discovery in Pythoncited by 109
- 2022Vertical Federated Learning: Challenges, Methodologies and Experimentscited by 55
- 2018Causal Discovery in the Presence of Missing Datacited by 47
- 2019Secure and Efficient Federated Transfer Learningcited by 21
- 2013Discovering Cyclic Causal Models with Latent Variables: A General SAT-Based Procedurecited by 18
- 2022GRACE-C: Generalized Rate Agnostic Causal Estimation via Constraintscited by 2
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