Benchmarking Counterfactual Prediction in Epidemic Time Series

Benchmarking Counterfactual Prediction in Epidemic Time Series with Time-Varying Interventions

arXiv:2606.05692v1 Announce Type: new Abstract: 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...