CausalCompass benchmarks TSCD methods across eight misspecification scenarios and finds deep learning approaches generally outperform others, with no single method dominating all cases.
On causal discovery from time series data using fci.Proba- bilistic graphical models, 16
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A procedure builds provably minimal Markovian states from a longitudinal causal graph, but deep RL requires multi-order historical state exposure (MOSE) to realize gains over minimal or fixed-window baselines.
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CausalCompass: Evaluating the Robustness of Time-Series Causal Discovery in Misspecified Scenarios
CausalCompass benchmarks TSCD methods across eight misspecification scenarios and finds deep learning approaches generally outperform others, with no single method dominating all cases.
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Integrating Causal DAGs in Deep RL: Activating Minimal Markovian States with Multi-Order Exposure
A procedure builds provably minimal Markovian states from a longitudinal causal graph, but deep RL requires multi-order historical state exposure (MOSE) to realize gains over minimal or fixed-window baselines.