TCD-Arena is a new customizable testing framework that runs millions of experiments to map how 33 different assumption violations affect time series causal discovery methods and shows ensembles can boost overall robustness.
Causal discovery with reinforcement learning
7 Pith papers cite this work. Polarity classification is still indexing.
7
Pith papers citing it
representative citing papers
DAG-DC-ADMM jointly clusters subjects and learns their cluster-specific causal DAGs via structural equation modeling, groupwise truncated Lasso fusion penalties, and an ADMM solver for the resulting nonconvex problem.
Matrix-weighted regularization for robust multi-task regression achieves optimal MSE under weaker spectral assumptions and performs no worse than independent learning when balancedness is poor.
Pilot study uses pretrained video encoder features from lung ultrasound to predict 30-day CHF readmission, finding lower-lung views and temporal differences most informative with top MLP F1 of 0.80.
PICK adds a parent-finding subroutine for leaf nodes to speed up pruning in score-matching causal discovery, extending it from i.i.d. data to static and temporal network data.
Empirical evaluation on synthetic and real-world datasets indicates that natural experiments are present and can be leveraged via causal feature selection to boost model performance.
A polynomial trace constraint h_poly(W)=Σ tr((W◦W)^k) is shown equivalent to NOTEARS' exponential acyclicity constraint, and a geometric-series evaluation makes it faster at moderate node counts.
citing papers explorer
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TCD-Arena: Assessing Robustness of Time Series Causal Discovery Methods Against Assumption Violations
TCD-Arena is a new customizable testing framework that runs millions of experiments to map how 33 different assumption violations affect time series causal discovery methods and shows ensembles can boost overall robustness.
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A Unified Framework for Structure-Aware Clustering and Heterogeneous Causal Graph Learning
DAG-DC-ADMM jointly clusters subjects and learns their cluster-specific causal DAGs via structural equation modeling, groupwise truncated Lasso fusion penalties, and an ADMM solver for the resulting nonconvex problem.
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Multi-task Linear Regression without Eigenvalue Lower Bounds: Adaptivity, Robustness, and Safety
Matrix-weighted regularization for robust multi-task regression achieves optimal MSE under weaker spectral assumptions and performs no worse than independent learning when balancedness is poor.
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Prognostic Value of Lung Ultrasound Biomarkers for Readmission Risk in Congestive Heart Failure: A Pilot Data-Driven Analysis
Pilot study uses pretrained video encoder features from lung ultrasound to predict 30-day CHF readmission, finding lower-lung views and temporal differences most informative with top MLP F1 of 0.80.
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Score-matching-based Structure Learning for Temporal Data on Networks
PICK adds a parent-finding subroutine for leaf nodes to speed up pruning in score-matching causal discovery, extending it from i.i.d. data to static and temporal network data.
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Do Real-World Datasets Contain Natural Experiments? An Empirical Study Using Causal Feature Selection
Empirical evaluation on synthetic and real-world datasets indicates that natural experiments are present and can be leveraged via causal feature selection to boost model performance.
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polyDAG: Polynomial Acyclicity Constraints for Efficient Continuous Causal Discovery in Visual Semantic Graphs
A polynomial trace constraint h_poly(W)=Σ tr((W◦W)^k) is shown equivalent to NOTEARS' exponential acyclicity constraint, and a geometric-series evaluation makes it faster at moderate node counts.