CausalCompass benchmarks TSCD methods across eight misspecification scenarios and finds deep learning approaches generally outperform others, with no single method dominating all cases.
Survey and evaluation of causal discovery methods for time series.Journal of Artificial Intelligence Research, 73:767–819
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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.
Presents a causal inference framework for autonomous robot decision-making on task execution timing and strategy using estimates of battery usage and human obstructions, evaluated via a new Gazebo simulator called PeopleFlow against a non-causal baseline in a warehouse setting.
citing papers explorer
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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.
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Causality-enhanced Decision-Making for Autonomous Mobile Robots in Dynamic Environments
Presents a causal inference framework for autonomous robot decision-making on task execution timing and strategy using estimates of battery usage and human obstructions, evaluated via a new Gazebo simulator called PeopleFlow against a non-causal baseline in a warehouse setting.