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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.

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4 results for “LPCMCI”

Observational and Interventional Causal Learning for Regret-Minimizing Controlstat.ML

Extended LPCMCI learns causal models from interventional data to minimize prediction error.

problem Optimizing prediction of target variables using causal models.

method Combining observational and interventional causal discovery methods.

result Extended LPCMCI allows 60.9% optimal prediction of target variables compared to 53.6% with original LPCMCI.

causal discovery regret minimization interventional data prediction error time series analysis

2022-12-05abs ↗pdf ↗

Causal discovery for time series with latent confoundersstat.ML

Causal relationships in time series with latent variables are discovered using LPCMCI.

problem Discovering causal relationships in complex, time-series data with hidden variables.

method Evaluated LPCMCI algorithm for finding generators compatible with multi-dimensional, autocorrelated time series with latent variables.

result LPCMCI performs better than random guessing but is not optimal.

causal discovery time series latent confounders LPCMCI autocorrelation

2022-09-07abs ↗pdf ↗

Linkages among the Foreign Exchange, Stock, and Bond Markets in Japan and the United Statesq-fin.ST

Study explores financial market linkages between Japan and US markets.

problem Inconsistency in empirical studies regarding financial market causal linkages.

method Causal discovery methods including VAR-LiNGAM and LPCMCI with domain knowledge.

result VAR-LiNGAM reveals causal influences among financial markets, while LPCMCI identifies potential latent confounders.

financial markets Japan US VAR-LiNGAM LPCMCI

2023-10-04abs ↗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 ↗