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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Investigating Causal Relations by Econometric Models and Cross-spectral Methods
Canonical reference. 83% of citing Pith papers cite this work as background.
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83% of classified citations
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UNVERDICTED 16representative citing papers
A latent state model of real chat logs shows chatbots sustain delusional beliefs longer than humans initiate them, forming feedback loops where chatbot self-influence dominates over time.
Causal analysis of water MD simulations shows translational motions drive orientational dynamics in supercooled HDL but remain decoupled at ambient conditions, revealing an emergent arrow of time in fluctuation couplings.
CausalMoE is a multimodal foundation model with pattern-routed heterogeneous experts and LLM/VLM integration that claims new SOTA performance on supervised and few-shot Granger causal discovery benchmarks.
MF-Net learns a shared field state and mechanical transition rule from trajectories to deliver competitive forecasting and recoverable relation matrices on Lorenz-96 and real systems.
Bitcoin price power law is rejected on distributional series and not robust to time-origin shifts, but dominates medium-horizon forecasts against baselines because it avoids committing to specific wave shapes.
Stratospheric polar vortex predictability is multimodal, with short-term forecasts dominated by persistence of the leading state and extended forecasts arising from higher-order stratospheric structures plus tropospheric variability.
SVAR-FM uses simulator clamping to produce interventional distributions and flow matching to identify time series causal structures, with an error bound that predicts sign reversal of causal effects below a simulator accuracy threshold.
PerCaM-Health learns evolving personalized dynamic causal graphs from longitudinal health data to enable more reliable patient-level counterfactual queries than cohort or per-patient baselines.
M-CaStLe generalizes local stencil-based causal discovery to the multivariate case and decomposes resulting graphs into reaction and spatial components for interpretation in space-time gridded data.
Dynamic directed spectral co-clustering on degree-corrected stochastic co-blockmodels embedded in VAR-type models uncovers latent community paths, with non-asymptotic misclassification bounds and applications to U.S. payrolls and global stock volatilities.
DGF explicitly models multiple mode-conditioned predictive distributions via Dirichlet-guided sampling and reward optimization to preserve dynamical features in time series forecasts.
SGED-TCD is a lag-resolved causal discovery framework that uses structural gating and perturbation-effect alignment to infer interpretable weighted causal networks from complex time series, shown on heat-pollution extremes in China.
Renzo liquid restaking revenue is primarily predicted by EigenLayer value locked, token yield, and multi-blockchain expansion, with current bridge risks not imposing systemic threats to the restaking ecosystem.
A proposed Bayesian architecture that initializes physiological set-point priors from GWAS-derived genomic anchors and decays them toward empirical baselines as data accrue.
The paper reviews existing signal processing strategies for brain-heart interactions, their usability in biomarker development, and key challenges for future work.
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.
-
The Dynamics of Delusion: Modeling Bidirectional False Belief Amplification in Human-Chatbot Dialogue
A latent state model of real chat logs shows chatbots sustain delusional beliefs longer than humans initiate them, forming feedback loops where chatbot self-influence dominates over time.
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Causality in Liquid Water as a Hallmark of Emergent Glassy Dynamics
Causal analysis of water MD simulations shows translational motions drive orientational dynamics in supercooled HDL but remain decoupled at ambient conditions, revealing an emergent arrow of time in fluctuation couplings.
-
CausalMoE: A Billion-Scale Multimodal Foundation Model for Granger Causal Discovery with Pattern-Routed Heterogeneous Experts
CausalMoE is a multimodal foundation model with pattern-routed heterogeneous experts and LLM/VLM integration that claims new SOTA performance on supervised and few-shot Granger causal discovery benchmarks.
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Mechanical Field Networks: Structured Neural Dynamics for Multivariate Systems
MF-Net learns a shared field state and mechanical transition rule from trajectories to deliver competitive forecasting and recoverable relation matrices on Lorenz-96 and real systems.
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Bitcoin's Power Law: Weak Structure, Strong Forecasts
Bitcoin price power law is rejected on distributional series and not robust to time-origin shifts, but dominates medium-horizon forecasts against baselines because it avoids committing to specific wave shapes.
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State-resolved multimodal contributions to stratospheric polar vortex predictability
Stratospheric polar vortex predictability is multimodal, with short-term forecasts dominated by persistence of the leading state and extended forecasts arising from higher-order stratospheric structures plus tropospheric variability.
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Intervention-Based Time Series Causal Discovery via Simulator-Generated Interventional Distributions
SVAR-FM uses simulator clamping to produce interventional distributions and flow matching to identify time series causal structures, with an error bound that predicts sign reversal of causal effects below a simulator accuracy threshold.
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PerCaM-Health: Personalized Dynamic Causal Graphs for Healthcare Reasoning
PerCaM-Health learns evolving personalized dynamic causal graphs from longitudinal health data to enable more reliable patient-level counterfactual queries than cohort or per-patient baselines.
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M-CaStLe: Uncovering Local Causal Structures in Multivariate Space-Time Gridded Data
M-CaStLe generalizes local stencil-based causal discovery to the multivariate case and decomposes resulting graphs into reaction and spatial components for interpretation in space-time gridded data.
-
Latent community paths in VAR-type models via dynamic directed spectral co-clustering
Dynamic directed spectral co-clustering on degree-corrected stochastic co-blockmodels embedded in VAR-type models uncovers latent community paths, with non-asymptotic misclassification bounds and applications to U.S. payrolls and global stock volatilities.
-
Dirichlet-Guided Group Forecasting for Alleviating Over-smoothing in Time Series Forecasting
DGF explicitly models multiple mode-conditioned predictive distributions via Dirichlet-guided sampling and reward optimization to preserve dynamical features in time series forecasts.
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Structural Gating and Effect-aligned Lag-resolved Temporal Causal Discovery Framework with Application to Heat-Pollution Extremes
SGED-TCD is a lag-resolved causal discovery framework that uses structural gating and perturbation-effect alignment to infer interpretable weighted causal networks from complex time series, shown on heat-pollution extremes in China.
-
Financial Dynamics and Interconnected Risk of Liquid Restaking
Renzo liquid restaking revenue is primarily predicted by EigenLayer value locked, token yield, and multi-blockchain expansion, with current bridge risks not imposing systemic threats to the restaking ecosystem.
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Is It You or Your Environment? A Bayesian Inference Framework for Genomically-Anchored Personalized Physiological Interpretation
A proposed Bayesian architecture that initializes physiological set-point priors from GWAS-derived genomic anchors and decays them toward empirical baselines as data accrue.
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Measures and Models of Brain-Heart Interactions
The paper reviews existing signal processing strategies for brain-heart interactions, their usability in biomarker development, and key challenges for future work.