New Benchmark Dataset Developed for Evaluating Causal Inference Methods in Epidemic Forecasting
A team of researchers has developed a large-scale benchmark dataset for testing deep learning methods that predict counterfactual outcomes in epidemic time series under dynamic intervention policies. The benchmark uses a calibrated agent-based model drawing on real-world demographic, mobility, epidemiological, and policy data to generate realistic counterfactual trajectories across more than 150 U.S. counties. The work addresses a critical gap in causal inference research, where the absence of ground-truth counterfactuals has long hindered rigorous evaluation of AI methods used to assess the effects of public health interventions.
Accepted to the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2026), this study introduces a benchmark designed to overcome a fundamental limitation in time-series causal inference: the inability to observe what would have happened under alternative policy decisions. Existing approaches either use real-world data lacking ground-truth counterfactuals or rely on overly simplified simulations that do not reflect complex causal dynamics. The new benchmark supports static and time-varying treatments as well as single-policy and multi-policy intervention settings, enabling evaluation across a broad range of causal inference scenarios. By grounding the agent-based simulation in real-world data spanning demographics, human mobility, epidemiology, and historical policy records, the researchers generate synthetic but realistic epidemic trajectories for over 150 U.S. counties. Evaluations of widely used and state-of-the-art causal inference methods on this benchmark reveal substantial performance differences, underscoring how challenging realistic causal reasoning in epidemic time series remains for current deep learning approaches.
What's missing
The benchmark's generalizability beyond U.S. counties — for example, to countries with different data infrastructures or epidemic dynamics — is not discussed in the available abstract. Limitations around the fidelity of the agent-based model relative to true real-world complexity, and potential sensitivity to calibration assumptions, are open questions not addressed in the available abstract.
What different sources said
- arXiv cs.AICenter
Benchmarking Counterfactual Prediction in Epidemic Time Series with Time-Varying Interventions
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