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 epidemic time series under dynamic interventions. Unlike existing benchmarks, it supports static and tim
The increasing sophistication of deep learning in time-series analysis and the need for more robust causal inference models, especially post-pandemic, drive the development of advanced benchmarks.
Improved counterfactual prediction in epidemic modeling can significantly enhance public health responses and resource allocation during future crises, providing more accurate foresight into intervention effectiveness.
The availability of a realistic, large-scale benchmark with observable counterfactual outcomes will accelerate research and development in causal AI for complex dynamic systems beyond epidemics.
- · AI researchers
- · Public health organizations
- · Governments
- · Epidemiologists
- · Traditional statistical modeling approaches
- · Limited-scope simulation models
More accurate predictive models for policy impact on complex time-varying systems.
Reduced economic and social disruption from future epidemics due to data-driven proactive interventions.
The methodology could generalize to other critical domains like climate modeling or financial stability, enhancing foresight in various sectors.
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Read at arXiv cs.LG