10 citations · 16 across the 5 of their papers we have counts for
6 papers · 1 filter
Matching a Desired Causal State via Shift Interventions
Jiaqi Zhang, Chandler Squires, Caroline Uhler
Transforming a causal system from a given initial state to a desired target state is an important task permeating multiple fields including control theory, biology, and materials s…
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…
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…
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…
Direct Estimation of Differences in Causal Graphs
Yuhao Wang, Chandler Squires, Anastasiya Belyaeva +1
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. T…