Chandler Squires
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  • Publications
    • Concept Modulation Models: A Unified Framework for Identifiability and Extrapolation
    • Maximum Likelihood Estimation for Brownian Motion Tree Models Based on One Sample
    • Context-Informed Ship Trajectory Prediction via Conditional Attention
    • Catastrophic Compositional Generation: Why Vanilla Diffusion Models Fail to Extrapolate
    • A Unifying Framework for Unsupervised Concept Extraction
    • Causal Representation Learning
    • Knowledge-Enriched Machine Learning for Tabular Data
    • Probably Approximately Correct High-dimensional Causal Effect Estimation Given a Valid Adjustment Set
    • The Landscape of Causal Discovery Data: Grounding Causal Discovery in Real-World Applications
    • Synthetic Potential Outcomes and Causal Mixture Identifiability
    • Causal Foundations for Pragmatic Data Science
    • Causal Imputation for Counterfactual SCMs: Bridging Graphs and Latent Factor Models
    • Linear Causal Disentanglement via Interventions
    • The DeCAMFounder: Non-Linear Causal Discovery in the Presence of Hidden Variables
    • Causal Structure Learning: a Combinatorial Perspective
    • Causal Imputation via Synthetic Interventions
    • Causal Structure Discovery between Clusters of Nodes Induced by Latent Factors
    • Matching a Desired Causal State via Shift Interventions
    • DCI: Learning Causal Differences between Gene Regulatory Networks
    • Causal Network Models of SARS-CoV-2 Expression and Aging to Identify Candidates for Drug Repurposing
    • Active Structure Learning of Causal DAGs via Directed Clique Trees
    • Ordering-Based Causal Structure Learning in the Presence of Latent Variables
    • Permutation-Based Causal Structure Learning with Unknown Intervention Targets
    • Size of Interventional Markov Equivalence Classes in Random DAG Models
    • ABCD-Strategy: Budgeted Experimental Design for Targeted Causal Structure Discovery
    • Direct Estimation of Differences in Causal Graphs
    • Active Learning for Optimal Intervention Design in Causal Models
    • Identifiability Guarantees for Causal Disentanglement from Soft Interventions
    • Unpaired Multi-Domain Causal Representation Learning
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Identifiability Guarantees for Causal Disentanglement from Soft Interventions

Jiaqi Zhang
,
Kristjan Greenewald
Chandler Squires
Chandler Squires
,
Akash Srivastava
,
Karthikeyan Shanmugam
,
Caroline Uhler
· 0 min read
Cite
Chandler Squires
Authors
Chandler Squires
Postdoctoral Research Associate
← Active Learning for Optimal Intervention Design in Causal Models Jan 1, 0001
Unpaired Multi-Domain Causal Representation Learning Jan 1, 0001 →

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