PAIR-CI restores calibration to conditional independence testing under missing data by using paired permutations that force imputation error to cancel in the loss difference, together with a consistent variance estimator that unifies cross-validation and imputation uncertainty.
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4 Pith papers cite this work. Polarity classification is still indexing.
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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.
DAGgr aggregates weighted candidate DAGs using out-of-sample predictive likelihood and an acyclicity-preserving threshold, with claimed finite-sample bounds and consistency, outperforming baselines in simulations and protein network data.
TriOpt recovers topological ordering via Sherman-Morrison rank-1 updates on linear kernels and then solves a convex continuous optimization for the linear DAG structure.
citing papers explorer
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PAIR-CI: Calibrated Conditional Independence Testing for Causal Discovery with Incomplete Data
PAIR-CI restores calibration to conditional independence testing under missing data by using paired permutations that force imputation error to cancel in the loss difference, together with a consistent variance estimator that unifies cross-validation and imputation uncertainty.
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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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Stable Causal Discovery via Directed Acyclic Graph Aggregation
DAGgr aggregates weighted candidate DAGs using out-of-sample predictive likelihood and an acyclicity-preserving threshold, with claimed finite-sample bounds and consistency, outperforming baselines in simulations and protein network data.
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TriOpt: A Scalable Algorithm for Linear Causal Discovery
TriOpt recovers topological ordering via Sherman-Morrison rank-1 updates on linear kernels and then solves a convex continuous optimization for the linear DAG structure.