Chandler Squires

16 papers · 2018–2025 · 5 conferences · across top CS/AI conferences

Achievements

+11 more ↓

🏃 Academic Marathon (7) 🌉 Interdisciplinary Bridge 🌍 Conference Polyglot (5) 🧭 Keyword Pioneer 🐝 Cross-Pollinator (12)

🌈 Renaissance Researcher (6) 🌍 Conference Polyglot (5) 🏃 Academic Marathon (7) 🤝 Dynamic Duo (13) 🔬 Deep Specialist (12) 🧬 Topic Evolution 🗃️ Keyword Collector (51) 🚀 Conference Pioneer 📈 Trend Setter 🔥 Unstoppable (8) 💎 Century Club (16)

Conferences

CLEAR (5) NIPS (5) AISTATS (4) ICML (1) UAI (1)

Top co-authors

Caroline Uhler (13) Karthikeyan Shanmugam (4) Dmitriy Katz (2) Yuhao Wang (2) Jiaqi Zhang (2) Kristjan Greenewald (2) Raj Agrawal (2) Philippe Brouillard (1) Jonas Wahl (1) Salil S Bhate (1)

Keywords

causal inference (6) causal discovery (5) latent variable (4) directed acyclic graph (4) causal graph (3) interventional datum (3) structure learning (2) causal disentanglement (2) gene regulatory network (2) markov equivalence class (2) causal model (2) causal structure (2) latent factor model (2) causal dag (2) experimental design (1) gene expression (1) structural learning (1) causal structure learning (1) causal reasoning (1) active learning (1)

Papers

The Landscape of Causal Discovery Data: Grounding Causal Discovery in Real-World Applications CLEAR 2025 Probably approximately correct high-dimensional causal effect estimation given a valid adjustment set CLEAR 2025 Synthetic Potential Outcomes and Causal Mixture Identifiability AISTATS 2025 Causal Imputation for Counterfactual SCMs: Bridging Graphs and Latent Factor Models CLEAR 2024 Unpaired Multi-Domain Causal Representation Learning NIPS 2023 Identifiability Guarantees for Causal Disentanglement from Soft Interventions NIPS 2023 Linear Causal Disentanglement via Interventions ICML 2023 Causal Structure Discovery between Clusters of Nodes Induced by Latent Factors CLEAR 2022 Causal Imputation via Synthetic Interventions CLEAR 2022 Matching a Desired Causal State via Shift Interventions NIPS 2021 Permutation-Based Causal Structure Learning with Unknown Intervention Targets UAI 2020 Active Structure Learning of Causal DAGs via Directed Clique Trees NIPS 2020 Ordering-Based Causal Structure Learning in the Presence of Latent Variables AISTATS 2020 ABCD-Strategy: Budgeted Experimental Design for Targeted Causal Structure Discovery AISTATS 2019 Size of Interventional Markov Equivalence Classes in random DAG models AISTATS 2019 Direct Estimation of Differences in Causal Graphs NIPS 2018