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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.

168,982 papers · 148 categories

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6 results for “Causal-Correlation”

Causality-Aware Spatiotemporal Graph Neural Networks for Spatiotemporal Time Series Imputationcs.LG

Casper uses causal graph neural networks to improve spatiotemporal time series imputation.

problem Imputing missing values in spatiotemporal time series with confounders and non-causal correlations.

method Casper introduces a novel Prompt Based Decoder (PBD) and Spatiotemporal Causal Attention (SCA) to block confounders and discover causal relationships.

result Casper outperforms baselines and effectively discovers causal relationships in spatiotemporal time series imputation.

spatiotemporal graph neural networks time series imputation causal inference frontdoor adjustment

2024-03-18abs ↗pdf ↗

Bidirectional RNN-based Few-shot Training for Detecting Multi-stage Attackcs.CR

This paper detects multi-stage Feint Attacks using Bi-RNN and few-shot learning.

problem Detecting multi-stage Feint Attacks due to lack of professional datasets and semantic relationships.

method Fuzzy clustering for attack chain mining, few-shot deep learning, Bi-RNN for feature extraction.

result Accurately detected Feint Attacks using Bi-RNN and few-shot learning.

Feint Attack Multi-stage Attack Few-shot Learning Bi-RNN Causal-Correlation

2019-05-09abs ↗pdf ↗

Structural Disentanglement of Causal and Correlated Conceptscs.LG

Unified framework for generating data by modeling causal and correlational dependencies.

problem Modeling both causal and correlational dependencies among latent factors.

method Causal-Correlation Variational Autoencoder (C2VAE) framework.

result Improves generation quality, disentanglement, and intervention fidelity.

causal modeling correlation latent space variational autoencoder data generation

2024-05-25abs ↗pdf ↗

Multiple Causes: A Causal Graphical Viewstat.ML

Graphs help identify unobserved confounders in causal inference.

problem Unobserved confounders skew causal inference from observational data.

method Causal graphical view and probabilistic factor models.

result The deconfounder algorithm makes valid inferences of intervention distributions.

causal inference confounding multiple causes probabilistic factor models intervention distributions

2019-05-30abs ↗pdf ↗

Time and Frequency Structure of Causal Correlation Network in China Bond Marketq-fin.ST

There are more than eight hundred interest rates published in China bond market every day. Which are the benchmark interest rates that have broad influences on most interest rates is a major concern for economists. In this paper, multi-variable Granger causality test is developed and applied to construct a directed net…

2016-01-03abs ↗pdf ↗

Can predictive models be used for causal inference?stat.ML

Predictive models can be used for causal inference with feature selection.

problem Limitations of predictive models in interpreting causal relationships.

method Constrained learning process by selecting features according to Pearl's backdoor adjustment criterion.

result Causal models provide near unbiased effect estimates and better generalization.

causal inference predictive models feature selection deep learning machine learning

2023-06-18abs ↗pdf ↗