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Found 5 papers, 2 papers with code
On the Representation of Causal Background Knowledge and its Applications in Causal Inference
Causal background knowledge about the existence or the absence of causal edges and paths is frequently encountered in observational studies.
A Local Method for Identifying Causal Relations under Markov Equivalence
We propose a local approach to identify whether a variable is a cause of a given target under the framework of causal graphical models of directed acyclic graphs (DAGs).
On Low Rank Directed Acyclic Graphs and Causal Structure Learning
Despite several advances in recent years, learning causal structures represented by directed acyclic graphs (DAGs) remains a challenging task in high dimensional settings when the graphs to be learned are not sparse.
A Graph Autoencoder Approach to Causal Structure Learning
Causal structure learning has been a challenging task in the past decades and several mainstream approaches such as constraint- and score-based methods have been studied with theoretical guarantees.
Masked Gradient-Based Causal Structure Learning
This paper studies the problem of learning causal structures from observational data.
