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 …
Latent hierarchical causal structure discovery with rank constraints
Most causal discovery procedures assume that there are no latent confounders in the
system, which is often violated in real-world problems. In this paper, we consider a …
system, which is often violated in real-world problems. In this paper, we consider a …
Identification of linear non-gaussian latent hierarchical structure
Traditional causal discovery methods mainly focus on estimating causal relations among
measured variables, but in many real-world problems, such as questionnaire-based …
measured variables, but in many real-world problems, such as questionnaire-based …
Python package for causal discovery based on LiNGAM
Causal discovery is a methodology for learning causal graphs from data, and LiNGAM is a
well-known model for causal discovery. This paper describes an open-source Python …
well-known model for causal discovery. This paper describes an open-source Python …
Causal discovery from observational and interventional data across multiple environments
A fundamental problem in many sciences is the learning of causal structure underlying a
system, typically through observation and experimentation. Commonly, one even collects …
system, typically through observation and experimentation. Commonly, one even collects …
Unpaired multi-domain causal representation learning
The goal of causal representation learning is to find a representation of data that consists of
causally related latent variables. We consider a setup where one has access to data from …
causally related latent variables. We consider a setup where one has access to data from …
On the convergence of continuous constrained optimization for structure learning
Recently, structure learning of directed acyclic graphs (DAGs) has been formulated as a
continuous optimization problem by leveraging an algebraic characterization of acyclicity …
continuous optimization problem by leveraging an algebraic characterization of acyclicity …
Structure learning with continuous optimization: A sober look and beyond
This paper investigates in which cases continuous optimization for directed acyclic graph
(DAG) structure learning can and cannot perform well and why this happens, and suggests …
(DAG) structure learning can and cannot perform well and why this happens, and suggests …
Identification of linear latent variable model with arbitrary distribution
An important problem across multiple disciplines is to infer and understand meaningful
latent variables. One strategy commonly used is to model the measured variables in terms of …
latent variables. One strategy commonly used is to model the measured variables in terms of …
Adaptive skeleton construction for accurate DAG learning
Directed acyclic graph (DAG) learning plays a key role in causal discovery and many
machine learning tasks. Learning a DAG from high-dimensional data always faces …
machine learning tasks. Learning a DAG from high-dimensional data always faces …