CausalCompass benchmarks TSCD methods across eight misspecification scenarios and finds deep learning approaches generally outperform others, with no single method dominating all cases.
Causal network reconstruction from time series: From theoretical assumptions to practical estimation.Chaos: An Interdisciplinary Journal of Nonlinear Science, 28(7)
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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.