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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,695 papers · 148 categories

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12 results for “PCMCI+”

Discovering contemporaneous and lagged causal relations in autocorrelated nonlinear time series datasetsstat.ME

Novel method discovers causal relations in time series data, even with autocorrelation.

problem Discovering causal relations in time series data with strong autocorrelation.

method Conditional independence (CI) based PCMCI

^+

method, optimized for contemporaneous and lagged links.

result PCMCI

^+

outperforms other methods in detecting causal links and controlling false positives.

causal discovery time series autocorrelation conditional independence contemporaneous links

2020-03-07abs ↗pdf ↗

Bootstrap aggregation and confidence measures to improve time series causal discoverystat.ME

Improved time series causal discovery with bootstrap aggregation and confidence measures.

problem Uncertainty estimation in time series causal discovery.

method Bootstrap aggregation and confidence measures for time series causal discovery.

result Bagged-PCMCI+ improves precision and recall compared to PCMCI+.

time series causal discovery bootstrap aggregation confidence measures PCMCI+statistical performance

2023-06-15abs ↗pdf ↗

Causal Discovery in Semi-Stationary Time Seriescs.LG

New algorithm uncovers causal relations in non-stationary time series.

problem Discovering causal relations from non-stationary time series data.

method Constraint-based, non-parametric algorithm for semi-stationary time series.

result Algorithm PCMCI

_Ω

identifies causal graph with CI tests.

causal discovery non-stationary time series semi-stationary time series constraint-based PCMCI$_Ω$

2024-07-10abs ↗pdf ↗

Complex Dynamics in Psychological Data: Mapping Individual Symptom Trajectories to Group-Level Patternsstat.AP

Study integrates causal inference and temporal complexity measures to analyze mental health symptoms.

problem Examining how individual symptom trajectories reveal diagnostic patterns in mental disorders.

method Causal inference, graph analysis, temporal complexity measures, machine learning.

result 91% accuracy in diagnosing symptom dynamics, highlighting disorder-specific causal mechanisms.

causal inference temporal complexity mental health diagnostic accuracy psychopathology

2025-07-07abs ↗pdf ↗

Selecting Robust Features for Machine Learning Applications using Multidata Causal Discoverystat.ML

A new method selects robust features for ML models using causal discovery.

problem Challenges in feature selection for ML models with limited domain knowledge.

method Multidata causal feature selection using PC1 or PCMCI algorithms.

result The method improves model performance and provides interpretable drivers.

Feature selection Machine Learning Causal discovery Tropical Cyclones Conditional independence

2023-04-11abs ↗pdf ↗

Temporal Causal Prior-Data Fitted Networks for Panel Data with Learned Reliability Signalscs.LG

Temporal Causal Prior-Data Fitted Networks (TCPFN) for industrial time series causal discovery

problem Estimating causal effects in industrial time series

method Temporal Causal Prior-Data Fitted Networks

result Zero-shot causal discovery with explicit reliability signals

Causal Discovery Industrial Time Series Temporal Dynamics Reliability Signals

2026-06-18abs ↗pdf ↗

Causal-driven attribution (CDA): Estimating channel influence without user-level datastat.ML

CDA framework infers channel influence from aggregated data without user identifiers.

problem Lack of user-level path data due to privacy regulations and platform restrictions.

method CDA integrates PCMCI for causal discovery and Structural Causal Model for effect estimation.

result CDA achieves strong accuracy in estimating channel influence, even under structural uncertainty.

Attribution modelling Marketing effectiveness Causal discovery Privacy-preserving Temporal causal

2025-12-24abs ↗pdf ↗

TimeGraph: Synthetic Benchmark Datasets for Robust Time-Series Causal Discoverycs.LG

TimeGraph creates synthetic datasets for robust time-series causal discovery.

problem Lack of reliable synthetic benchmark datasets for robust time-series causal discovery.

method Developed comprehensive synthetic datasets with temporal properties, including trends, seasonality, and noise.

result Demonstrated significant variations in algorithm performance under realistic temporal conditions.

Time-series causal discovery Synthetic benchmark datasets Temporal properties Robustness Causal discovery algorithms

2025-06-02abs ↗pdf ↗

Path Signature Area-Based Causal Discovery in Coupled Time Seriesstat.ML

New method uses path signatures for causal discovery in time series data.

problem Challenges in understanding causal structure from observational time series data.

method Path signatures and signed areas for model-free causal discovery.

result Confidence sequence regions help identify lag/lead causal relationships.

path signatures causal discovery time series confidence sequences lag/lead relationships

2021-10-23abs ↗pdf ↗

Quantifying First-Order Markov Violations in Noisy Reinforcement Learning: A Causal Discovery Approachcs.LG

This paper detects Markov violations in RL with noise, improving policy development.

problem Partial observability and sensor/actuator noise invalidate Markovian assumptions in RL.

method Combines PCMCI causal discovery with Markov Violation score (MVS).

result Even substantial noise doesn't always disrupt multi-step dependencies.

Reinforcement Learning Markov Violations Partial Correlation Causal Discovery Sensor Noise

2025-02-28abs ↗pdf ↗

A Framework for Predictive Directional Trading Based on Volatility and Causal Inferenceq-fin.ST

This study proposes a framework for identifying profitable trading opportunities based on volatility and causal relationships.

problem Identifying profitable trading opportunities in financial markets.

method A combination of Gaussian Mixture Model (GMM), Granger Causality Test (GCT), Peter-Clark Momentary Conditional Independence (PCMCI) test, Dynamic Time Warping (DTW), and K-Nearest Neighbours (KNN) for identifying and executing trades.

result The proposed volatility-based trading strategy outperformed a Buy-and-Hold strategy, yielding a total return of 15.38%.

Financial Markets Volatility Causal Inference Trading Strategies Machine Learning

2025-07-12abs ↗pdf ↗

PRCD-MAP: Learning How Much to Trust Imperfect Priors in Causal Discoverystat.ML

PRCD-MAP learns to trust imperfect priors in causal discovery, improving accuracy and robustness.

problem Tackles the brittle trade-off between blind trust and rejection of external priors in causal discovery.

method Proposes PRCD-MAP, a soft prior-consumption layer that assigns per-edge trust to imperfect priors and modulates regularization in a MAP objective.

result Enjoys a population-level safety guarantee and outperforms existing methods on real-world causal discovery tasks.

causal discovery prior consumption soft priors trust calibration regularization

2026-05-03abs ↗pdf ↗