Introduces a Bayesian order-based learning method for multiple DAGs that uses heterogeneity to enhance causal ordering identifiability up to two permutations, with a new R2R proposal for efficient posterior sampling in high dimensions.
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An influence function projection approach exploits graph-implied conditional independences to improve the efficiency of semiparametric estimators for upper and lower bounds on average causal effects under sensitivity models for unmeasured confounding.
Prediction bottlenecks do not discover causal structure beyond what linear models, Lasso, and classical Granger/PCMCI methods achieve; intervention benefits are mostly sample-size confounds, leaving a standardized falsification benchmark as the main contribution.
A new directed tree structure learning framework for zero-inflated compositional nodes uses KL divergence scoring and column-stochastic transition matrices for conditional expectations, with proven consistency and finite-sample guarantees.
PRCD-MAP assigns per-edge trust to imperfect priors in causal discovery via empirical Bayes calibration and MLP propagation, delivering an ε-safety guarantee that vanishes at prior-quality extremes and empirical gains on CausalTime datasets.
FFML, TRFF, and FFCI are practical RFF-based approximations that replace expensive GP kernel matrices with finite feature maps, delivering competitive precision-recall trade-offs for score-based and constraint-based causal discovery in nonlinear mixed data.
TTCD uses a non-stationary feature learner and reconstruction-guided distillation inside a transformer to infer contemporaneous and lagged causal graphs from non-stationary time series without strong noise assumptions.
The authors introduce a validation framework showing LLMs can pull causal links from disaster social media but require checks against post-event evidence to avoid relying on model priors.
citing papers explorer
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Leveraging heterogeneity for identifiability: Bayesian order-based learning of multiple DAGs
Introduces a Bayesian order-based learning method for multiple DAGs that uses heterogeneity to enhance causal ordering identifiability up to two permutations, with a new R2R proposal for efficient posterior sampling in high dimensions.
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Exploiting independence constraints for efficient estimation of bounds on causal effects in the presence of unmeasured confounding
An influence function projection approach exploits graph-implied conditional independences to improve the efficiency of semiparametric estimators for upper and lower bounds on average causal effects under sensitivity models for unmeasured confounding.
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Prediction Bottlenecks Don't Discover Causal Structure (But Here's What They Actually Do)
Prediction bottlenecks do not discover causal structure beyond what linear models, Lasso, and classical Granger/PCMCI methods achieve; intervention benefits are mostly sample-size confounds, leaving a standardized falsification benchmark as the main contribution.
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Structure Learning for Directed Trees with Zero-Inflated Compositional Nodes
A new directed tree structure learning framework for zero-inflated compositional nodes uses KL divergence scoring and column-stochastic transition matrices for conditional expectations, with proven consistency and finite-sample guarantees.
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PRCD-MAP: Learning How Much to Trust Imperfect Priors in Causal Discovery
PRCD-MAP assigns per-edge trust to imperfect priors in causal discovery via empirical Bayes calibration and MLP propagation, delivering an ε-safety guarantee that vanishes at prior-quality extremes and empirical gains on CausalTime datasets.
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Fourier Feature Methods for Nonlinear Causal Discovery: FFML Scoring, TRFF Scoring, and FFCI Testing in Mixed Data
FFML, TRFF, and FFCI are practical RFF-based approximations that replace expensive GP kernel matrices with finite feature maps, delivering competitive precision-recall trade-offs for score-based and constraint-based causal discovery in nonlinear mixed data.
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TTCD:Transformer Integrated Temporal Causal Discovery from Non-Stationary Time Series Data
TTCD uses a non-stationary feature learner and reconstruction-guided distillation inside a transformer to infer contemporaneous and lagged causal graphs from non-stationary time series without strong noise assumptions.
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Large Language Models for Causal Relations Extraction in Social Media: A Validation Framework for Disaster Intelligence
The authors introduce a validation framework showing LLMs can pull causal links from disaster social media but require checks against post-event evidence to avoid relying on model priors.
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