Integrating Overlapping Static and Timeseries Causal Graphs via Constraint-Solving

In many causal learning problems, variables of interest are not always measured in the same observations, but are instead distributed across multiple datasets with overlapping variables. Previous algorithms used independence tests to directly determine the minimal equivalence class of DAGs consistent with all input graphs, but these methods faced significant computational limits. In this paper, we formulate this problem as a more computationally efficient answer set programming (ASP) problem called SCION (Solving Constraints for Integration of Overlapping Networks), which can be solved with the ASP system *clingo*. We then extend SCION to a novel method, SCION-TS, for solving the overlapping variables problem in the timeseries setting. We test both algorithms across multiple simulations to demonstrate their computational viability, and successfully apply both algorithms to input data, using p-value pooling to improve input graph estimation.