ION-C: Integration of Overlapping Networks via Constraints

Praveen Nair, Payal Anil Bhandari, Mohammadsajad Abavisani, Sergey M. Plis, David Danks

Keywords: Causal learning, Constraint satisfaction, Answer set programming, Social science data

TL;DR: We present and evaluate ION-C, an answer-set programming formulation of ION, a method for determining the set of potential ground-truth causal graphs consistent with a set of locally learned input graphs.

Abstract: 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 determining 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 $\textit{clingo}$. 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.

Primary Area: causal reasoning

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Submission Number: 5837