161 citations · 1k across the 69 of their papers we have counts for
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Causal Imputation for Counterfactual SCMs: Bridging Graphs and Latent Factor Models
Alvaro Ribot, Chandler Squires, Caroline Uhler
We consider the task of causal imputation, where we aim to predict the outcomes of some set of actions across a wide range of possible contexts. As a running example, we consider p…
Identifiability Guarantees for Causal Disentanglement from Soft Interventions
Jiaqi Zhang, Chandler Squires, Kristjan Greenewald +3
Causal disentanglement aims to uncover a representation of data using latent variables that are interrelated through a causal model. Such a representation is identifiable if the la…
Unpaired Multi-Domain Causal Representation Learning
Nils Sturma, Chandler Squires, Mathias Drton +1
The goal of causal representation learning is to find a representation of data that consists of causally related latent variables. We consider a setup where one has access to data…
Linear Causal Disentanglement via Interventions
Chandler Squires, Anna Seigal, Salil Bhate +1
Causal disentanglement seeks a representation of data involving latent variables that relate to one another via a causal model. A representation is identifiable if both the latent…
Joint Inference of Multiple Graphs from Matrix Polynomials
Madeline Navarro, Yuhao Wang, Antonio G. Marques +2
Inferring graph structure from observations on the nodes is an important and popular network science task. Departing from the more common inference of a single graph and motivated…
Causal Structure Discovery from Distributions Arising from Mixtures of DAGs
Basil Saeed, Snigdha Panigrahi, Caroline Uhler
We consider distributions arising from a mixture of causal models, where each model is represented by a directed acyclic graph (DAG). We provide a graphical representation of such…
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…
Differentially-Private Logistic Regression for Detecting Multiple-SNP Association in GWAS Databases
Fei Yu, Michal Rybar, Caroline Uhler +1
Following the publication of an attack on genome-wide association studies (GWAS) data proposed by Homer et al., considerable attention has been given to developing methods for rele…