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Leveraging a calibrated agent-based model grounded in real-world demographic, mobility, epidemiological, and policy data, we generate realistic counterfactual trajectories across more than 150 U.S.","title":"Benchmarking Counterfactual Prediction in Epidemic Time Series with Time-Varying Interventions","url":"https://arxiv.org/abs/2606.05692","vendor":"arxiv_cs_ai"},"summary":"arXiv:2606.05692v2 Announce Type: replace-cross \nAbstract: Deep learning has enabled significant advances in time-series causal inference, yet progress remains constrained by the lack of realistic benchmarks with observable counterfactual outcomes. Existing datasets either rely on real-world observations without ground-truth counterfactuals or on simplified simulations that fail to capture complex causal dynamics. To address this gap, we develop a large-scale benchmark for counterfactual prediction in epidemic time series under dynamic interventions. 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