CausalCompass benchmarks TSCD methods across eight misspecification scenarios and finds deep learning approaches generally outperform others, with no single method dominating all cases.
Causal discovery from incomplete data: A deep learning approach
3 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 3representative citing papers
MissNODAG is a differentiable cyclic causal graph learner that jointly recovers graph structure and missingness mechanism from incomplete data including MNAR via additive noise model and EM.
ReTimeCausal is a new EM-based alternating optimization method for causal discovery from irregularly sampled time series that claims consistency guarantees under high missingness.
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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MissNODAG: Differentiable Cyclic Causal Graph Learning from Incomplete Data
MissNODAG is a differentiable cyclic causal graph learner that jointly recovers graph structure and missingness mechanism from incomplete data including MNAR via additive noise model and EM.
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Causal Discovery for Irregularly Time Series with Consistency Guarantees
ReTimeCausal is a new EM-based alternating optimization method for causal discovery from irregularly sampled time series that claims consistency guarantees under high missingness.