A survey on causal reinforcement learning
While reinforcement learning (RL) achieves tremendous success in sequential decision-
making problems of many domains, it still faces key challenges of data inefficiency and the …
making problems of many domains, it still faces key challenges of data inefficiency and the …
Causal discovery for linear mixed data
Discovery of causal relationships from observational data, especially from mixed data that
consist of both continuous and discrete variables, is a fundamental yet challenging problem …
consist of both continuous and discrete variables, is a fundamental yet challenging problem …
A Review of Causal Methods for High-Dimensional Data
ZA Berkessa, E Läärä, P Waldmann - IEEE Access, 2024 - ieeexplore.ieee.org
Causal learning from observational data is an important scientific endeavor, but the
statistical and computational challenges posed by the high-dimensionality of many modern …
statistical and computational challenges posed by the high-dimensionality of many modern …
Probabilistic causal effect estimation with global neural network forecasting models
We introduce a novel method to estimate the causal effects of an intervention over multiple
treated units by combining the techniques of probabilistic forecasting with global forecasting …
treated units by combining the techniques of probabilistic forecasting with global forecasting …
Synergy-incorporated Bayesian Petri Net: A method for mining “AND/OR” relation and synergy effect with application in probabilistic reasoning
Bayesian networks (BNs) are widely used for knowledge representation and reasoning.
However, they suffer from the following limitations: 1) They are unable to explicitly learn …
However, they suffer from the following limitations: 1) They are unable to explicitly learn …
A practical approach to explaining defect proneness of code commits by causal discovery
Explainable software defect prediction is practical for software quality assurance. However, it
is hard to explain the predictions made by obscure machine learning models because of the …
is hard to explain the predictions made by obscure machine learning models because of the …
Linear Deconfounded Score Method: Scoring DAGs With Dense Unobserved Confounding
A Bellot, M van der Schaar - IEEE Transactions on Neural …, 2024 - ieeexplore.ieee.org
This article deals with the discovery of causal relations from a combination of observational
data and qualitative assumptions about the nature of causality in the presence of …
data and qualitative assumptions about the nature of causality in the presence of …
Causal discovery in linear structural causal models with deterministic relations
Linear structural causal models (SCMs)–in which each observed variable is generated by a
subset of the other observed variables as well as a subset of the exogenous sources–are …
subset of the other observed variables as well as a subset of the exogenous sources–are …
Counterfactual Covariate Causal Discovery on Nonlinear Extremal Quantiles
T Yin, H Chen, D Huang, H Wang - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Causality is an active relationship that transforms possibility into actuality, underscoring the
limitation of relying on averages to address rare events. This study proposes a …
limitation of relying on averages to address rare events. This study proposes a …
Nonlinear Causal Discovery via Dynamic Latent Variables
X Yang, T Lan, H Qiu, C Zhang - IEEE Transactions on …, 2025 - ieeexplore.ieee.org
Distinguishing causality from mere correlation is a cornerstone in empirical research, as
conflating the two can result in significant errors in decision-making, affecting policy …
conflating the two can result in significant errors in decision-making, affecting policy …