Breaking Predictions Is Not Enough: Specified-Foil Counterfactuals for Temporal Graphs
Read the original on arXiv AI →The paper introduces Specified-Foil Counterfactuals for temporal graphs, a method that seeks low‑cost past‑event interventions to make a user‑specified alternative outcome the top prediction. It uses trace‑guided search to compare completed executions of the original prediction with reconstructed incomplete executions of the foil, mapping differences to operations such as DELETE, INSERT, REWIRE, RELABEL, and SHIFT, and then verifies the foil through exact replay. Experiments on continuous‑time dynamic graphs and temporal knowledge graphs show that the approach retains most greedy successes while dramatically reducing predictor evaluations and achieving the specified foil in a majority of cases.
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