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Causal Deep Q Networks
Deep Q Networks (DQN) have shown remarkable success in various reinforcement learning tasks. However, their reliance on associative learning often...
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Probabilistic causal bipolar abstract argumentation: an approach based on credal networks
The Bipolar Argumentation Framework approach is an extension of the Abstract Argumentation Framework. A Bipolar Argumentation Framework considers a...
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Causal Networks
Directed graphs suggest themselves for an additional interpretation: Directed edges between nodes are intuitive and are often used to indicate causal...
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Using pre-trained models and graph convolution networks to find the causal relations among events in the Chinese financial text data
Nowadays, information explosion happens in every field. In the stock market of China, automatically understanding the market dynamics is extremely...
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Counterfactual Causal Adversarial Networks for Domain Adaptation
To eliminate domain shift in domain adaptation, most methods do so by encouraging the model to learn common features. However, the interpretability...
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Data Imputation with Adversarial Neural Networks for Causal Discovery from Subsampled Time Series
A relevant and challenging problem is causal discovery from time series data. This helps to understand dynamics events present in real world...
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Causal Interpretability and Uncertainty Estimation in Mixture Density Networks
Neural network implementations have predominantly been a black box lacking both in interpretability and estimation of uncertainty. In this study, we...
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High-dimensional causal discovery based on heuristic causal partitioning
Causal discovery is one of the most important research directions in the field of machine learning, aiming to discover the underlying causal...
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Controllable image generation based on causal representation learning
Artificial intelligence generated content (AIGC) has emerged as an indispensable tool for producing large-scale content in various forms, such as...
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Generalizable inductive relation prediction with causal subgraph
Inductive relation prediction is an important learning task for knowledge graph reasoning that aims to infer new facts from existing ones. Previous...
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Foundations of Causal ML
The present chapter covers the important dimension of causality in ML both in terms of causal structure discovery and causal inference. The vast...
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My Kingdom for a Causal Algorithm
This chapter delves into the critical need for reliable and interpretable causal algorithms in the contemporary landscape of artificial intelligence....
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Causal Disentanglement for Adversarial Defense
Representation learning that seeks the high accuracy of a classifier is a key contribute to the success of state-of-the-art DNNs. However, DNNs face...
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Pitfalls and Triumphs of Causal AI
This chapter delves into “Causal AI,” spotlighting achievements, ethics, and future directions. Centered on AI’s evolving aptitude for discerning...
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Multi-agent Learning of Causal Networks in the Internet of Things
In Internet of Things deployments, such as a smart home, building, or city, it is of paramount importance for software agents to be aware of the...
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Causal inference in the medical domain: a survey
Causal inference is considered a crucial topic in the medical field, as it enables the determination of causal effects for medical treatments through...
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A credible traffic prediction method based on self-supervised causal discovery
Next-generation wireless network aims to support low-latency, high-speed data transmission services by incorporating artificial intelligence (AI)...
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Granger causal representation learning for groups of time series
Discovering causality from multivariate time series is an important but challenging problem. Most existing methods focus on estimating the Granger...
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A Causal Inspired Early-Branching Structure for Domain Generalization
Learning domain-invariant semantic representations is crucial for achieving domain generalization (DG), where a model is required to perform well on...
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Evaluating the Usefulness of Counterfactual Explanations from Bayesian Networks
Bayesian networks are commonly used for learning with uncertainty and incorporating expert knowledge. However, they are hard to interpret, especially...
