10 citations · 15 across the 4 of their papers we have counts for
7 papers
Efficient Permutation Discovery in Causal DAGs
Chandler Squires, Joshua Amaniampong, Caroline Uhler
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 gra…
Active Structure Learning of Causal DAGs via Directed Clique Tree
Chandler Squires, Sara Magliacane, Kristjan Greenewald +3
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…
Ordering-Based Causal Structure Learning in the Presence of Latent Variables
Daniel Irving Bernstein, Basil Saeed, Chandler Squires +1
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 i…
Permutation-Based Causal Structure Learning with Unknown Intervention Targets
Chandler Squires, Yuhao Wang, Caroline Uhler
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…
Size of Interventional Markov Equivalence Classes in Random DAG Models
Dmitriy Katz, Karthikeyan Shanmugam, Chandler Squires +1
Directed acyclic graph (DAG) models are popular for capturing causal relationships. From observational and interventional data, a DAG model can only be determined up to its \emph{i…
ABCD-Strategy: Budgeted Experimental Design for Targeted Causal Structure Discovery
Raj Agrawal, Chandler Squires, Karren Yang +2
Determining the causal structure of a set of variables is critical for both scientific inquiry and decision-making. However, this is often challenging in practice due to limited in…