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

• URL: http://arxiv.org/abs/2606.05692v2
• Date: Thu, 11 Jun 2026 01:52:53 GMT
• Title: Benchmarking Counterfactual Prediction in Epidemic Time Series with Time-Varying Interventions
• Abstract summary: We develop a large-scale benchmark for counterfactual prediction in epidemic time series under dynamic interventions.<n>Unlike existing benchmarks, it supports static and time-varying treatments, as well as both single-policy and multi-policy intervention settings.<n>We generate realistic counterfactual trajectories across more than 150 U.S. counties.
• Score: 41.45415779643617
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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 time-varying treatments, as well as both single-policy and multi-policy intervention settings, enabling evaluation of causal inference methods across a broad range of causal inference scenarios. 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. counties. Using this benchmark, we evaluate widely used and state-of-the-art causal inference methods, revealing substantial performance differences and highlighting the challenges of realistic time-series causal reasoning.

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