Search Results for author: Chandler Squires

Found 11 papers, 7 papers with code

Linear Causal Disentanglement via Interventions

1 code implementation29 Nov 2022 Anna Seigal, Chandler Squires, Caroline Uhler

A representation is identifiable if both the latent model and the transformation from latent to observed variables are unique.

Disentanglement

Causal Structure Learning: a Combinatorial Perspective

no code implementations2 Jun 2022 Chandler Squires, Caroline Uhler

In this review, we discuss approaches for learning causal structure from data, also called causal discovery.

Causal Discovery

Matching a Desired Causal State via Shift Interventions

1 code implementation NeurIPS 2021 JiaQi Zhang, Chandler Squires, Caroline Uhler

In particular, we show that our strategies may require exponentially fewer interventions than the previously considered approaches, which optimize for structure learning in the underlying causal graph.

Active Learning

Active Structure Learning of Causal DAGs via Directed Clique Trees

1 code implementation NeurIPS 2020 Chandler Squires, Sara Magliacane, Kristjan Greenewald, Dmitriy Katz, Murat Kocaoglu, Karthikeyan Shanmugam

Most existing works focus on \textit{worst-case} or \textit{average-case} lower bounds for the number of interventions required to orient a DAG.

Selection bias

Efficient Permutation Discovery in Causal DAGs

no code implementations6 Nov 2020 Chandler Squires, Joshua Amaniampong, Caroline Uhler

We compare our method with $w = 1$ to algorithms for finding sparse elimination orderings of undirected graphs, and show that taking advantage of DAG-specific problem structure leads to a significant improvement in the discovered permutation.

Active Structure Learning of Causal DAGs via Directed Clique Tree

3 code implementations1 Nov 2020 Chandler Squires, Sara Magliacane, Kristjan Greenewald, Dmitriy Katz, Murat Kocaoglu, Karthikeyan Shanmugam

Most existing works focus on worst-case or average-case lower bounds for the number of interventions required to orient a DAG.

Selection bias

Ordering-Based Causal Structure Learning in the Presence of Latent Variables

no code implementations20 Oct 2019 Daniel Irving Bernstein, Basil Saeed, Chandler Squires, Caroline Uhler

We consider the task of learning a causal graph in the presence of latent confounders given i. i. d.~samples from the model.

Size of Interventional Markov Equivalence Classes in Random DAG Models

no code implementations5 Mar 2019 Dmitriy Katz, Karthikeyan Shanmugam, Chandler Squires, Caroline Uhler

For constant density, we show that the expected $\log$ observational MEC size asymptotically (in the number of vertices) approaches a constant.

Causal Inference Experimental Design

ABCD-Strategy: Budgeted Experimental Design for Targeted Causal Structure Discovery

3 code implementations27 Feb 2019 Raj Agrawal, Chandler Squires, Karren Yang, Karthik Shanmugam, Caroline Uhler

Determining the causal structure of a set of variables is critical for both scientific inquiry and decision-making.

Methodology

Direct Estimation of Differences in Causal Graphs

1 code implementation NeurIPS 2018 Yuhao Wang, Chandler Squires, Anastasiya Belyaeva, Caroline Uhler

We consider the problem of estimating the differences between two causal directed acyclic graph (DAG) models given i. i. d.~samples from each model.

Methodology

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