Causal Structure Learning

The DeCAMFounder: Non-Linear Causal Discovery in the Presence of Hidden Variables
Many real-world decision-making tasks require learning causal relationships between a set of variables. Typical causal discovery …
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Causal Structure Learning: a Combinatorial Perspective
In this review, we discuss approaches for learning causal structure from data, also called causal discovery. In particular, we focus on …
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Causal Structure Discovery between Clusters of Nodes Induced by Latent Factors
We consider the problem of learning the structure of a causal directed acyclic graph (DAG) model in the presence of latent variables. …
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Maximum Likelihood Estimation for Brownian Motion Tree Models Based on One Sample
We study the problem of maximum likelihood estimation given one data sample (n=1) over Brownian Motion Tree Models (BMTMs), a class of …
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Causal Network Models of SARS-CoV-2 Expression and Aging to Identify Candidates for Drug Repurposing
Given the severity of the SARS-CoV-2 pandemic, a major challenge is to rapidly repurpose existing approved drugs for clinical …
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Efficient Permutation Discovery in Causal DAGs
The problem of learning a directed acyclic graph (DAG) up to Markov equivalence is equivalent to the problem of finding a permutation …
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Active Structure Learning of Causal DAGs via Directed Clique Trees
A growing body of work has begun to study intervention design for efficient structure learning of causal directed acyclic graphs …
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Ordering-Based Causal Structure Learning in the Presence of Latent Variables
We consider the task of learning a causal graph in the presence of latent confounders given i.i.d. samples from the model. While …
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Permutation-Based Causal Structure Learning with Unknown Intervention Targets
We consider the problem of estimating causal DAG models from a mix of observational and interventional data, when the intervention …
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Direct Estimation of Differences in Causal Graphs
We consider the problem of estimating the differences between two causal directed acyclic graph (DAG) models with a shared topological …
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