<ul data-eligibleForWebStory="true"><li>Learning causal structures from time series data is challenging when the measurement frequency doesn't match the causal timescale.</li><li>Sub-sampling time series data can result in multiple equally possible causal graphs due to information loss.</li><li>Researchers are using answer set programming (ASP) to address the challenges of sub-sampling in deriving causal graphs.</li><li>ASP helps find the most probable underlying graph and provides an equivalence class of possible graphs for expert selection.</li><li>Using ASP and graph theory allows for faster and more accurate solutions compared to traditional approaches.</li><li>The approach was validated on simulated data and empirical brain connectivity, showing superiority over established methods.</li><li>The method achieved a 12% improvement in the F1 score compared to existing approaches.</li><li>State-of-the-art results were obtained in terms of precision and recall for reconstructing causal graphs from sub-sampled time series data.</li><li>The method displayed robustness to varying degrees of sub-sampling in realistic simulations.</li></ul>

Causal Graph Recovery in Neuroimaging through Answer Set Programming

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