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 machine learning: A survey and open problems
Causal Machine Learning (CausalML) is an umbrella term for machine learning methods
that formalize the data-generation process as a structural causal model (SCM). This …
that formalize the data-generation process as a structural causal model (SCM). This …
Causal reinforcement learning: A survey
Reinforcement learning is an essential paradigm for solving sequential decision problems
under uncertainty. Despite many remarkable achievements in recent decades, applying …
under uncertainty. Despite many remarkable achievements in recent decades, applying …
Causal parrots: Large language models may talk causality but are not causal
Some argue scale is all what is needed to achieve AI, covering even causal models. We
make it clear that large language models (LLMs) cannot be causal and give reason onto …
make it clear that large language models (LLMs) cannot be causal and give reason onto …
Causal deep learning
Causality has the potential to truly transform the way we solve a large number of real-world
problems. Yet, so far, its potential largely remains to be unlocked as causality often requires …
problems. Yet, so far, its potential largely remains to be unlocked as causality often requires …
Hindsight learning for mdps with exogenous inputs
Many resource management problems require sequential decision-making under
uncertainty, where the only uncertainty affecting the decision outcomes are exogenous …
uncertainty, where the only uncertainty affecting the decision outcomes are exogenous …
Causal deep learning: encouraging impact on real-world problems through causality
Causality has the potential to truly transform the way we solve a large number of real-world
problems. Yet, so far, its potential largely remains to be unlocked as causality often requires …
problems. Yet, so far, its potential largely remains to be unlocked as causality often requires …
Provably efficient causal model-based reinforcement learning for systematic generalization
In the sequential decision making setting, an agent aims to achieve systematic
generalization over a large, possibly infinite, set of environments. Such environments are …
generalization over a large, possibly infinite, set of environments. Such environments are …
Learning good interventions in causal graphs via covering
We study the causal bandit problem that entails identifying a near-optimal intervention from
a specified set A of (possibly non-atomic) interventions over a given causal graph. Here, an …
a specified set A of (possibly non-atomic) interventions over a given causal graph. Here, an …
Disentangled representations for causal cognition
Complex adaptive agents consistently achieve their goals by solving problems that seem to
require an understanding of causal information, information pertaining to the causal …
require an understanding of causal information, information pertaining to the causal …