Causal Structure Learning

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 of the variables that induces the sparsest graph. Without additional assumptions, this task is known to be NP-hard. …

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 (DAGs). A typical setting is a causally sufficient setting, i.e. a system with no latent confounders, selection bias, or …

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 current algorithms for causal structure discovery in the presence of latent confounders are constraint-based, we here …

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 targets are partially or completely unknown. This problem is highly relevant for example in genomics, since gene …

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 order given i.i.d. samples from each model. This is of interest for example in genomics, where changes in the …