Skip to content
conftrace_

Yonghan Jung

13 papers · 2020–2026 · 5 venues · across top CS/AI venues

Authors are matched by name, so researchers who share a name ("Yang Liu", "Wei Wang") appear as a single profile and their paper counts, venues and achievements are combined. How identity is handled.

Achievements

Jump to papers ↓
+5 more ↓ 🐣 Hot Topic Early Bird 🌍 Conference Polyglot Β· Silver πŸƒ Academic Marathon Β· Sprint πŸŒ‰ Interdisciplinary Bridge Β· Bronze πŸ—ΊοΈ Taxonomy Completionist (14)
🧬 Topic Evolution πŸ”¬ Deep Specialist (11) πŸ—ƒοΈ Keyword Collector (51) πŸ’Ž Century Club Β· Bronze πŸ”₯ Unstoppable Β· Bronze

Venues

NeurIPS (6) ICML (3) AAAI (2) CLeaR (1) CVPR (1)

Top co-authors

Jin Tian (9) Elias Bareinboim (9) Patrick Bloebaum (1) Bogyeong Kang (1) Min Woo Park (1) Kyungwoo Song (1) Ivan Diaz (1) Alexis Bellot (1) Subeen Park (1) Krikamol Muandet (1)

Research topics

Artificial Intelligence (11) Core AI (11) Machine Learning (10) Optimization & Theory (6) Core Methods (4) Learning Types (4) Knowledge & Reasoning (3) Reasoning (3) Application Areas (1)

Keywords

causal inference (10) causal effect estimation (4) double machine learning (3) graphical model (2) propensity score (2) causal identification (2) covariate adjustment (2) influence function (2) interventional distribution (2) causal effect (1) observational study (1) distribution shift (1) density estimation (1) policy evaluation (1) kernel smoothing (1) statistical estimation (1) model misspecification (1) group distributionally robust optimization (1) external validity (1) semiparametric estimation (1)

Papers

Information-Theoretic Causal Bounds under Unmeasured Confounding CLeaR 2026 Sufficient Invariant Learning for Distribution Shift CVPR 2025 Complete Graphical Criterion for Sequential Covariate Adjustment in Causal Inference NeurIPS 2024 Efficient Policy Evaluation Across Multiple Different Experimental Datasets NeurIPS 2024 Unified Covariate Adjustment for Causal Inference NeurIPS 2024 Estimating Causal Effects Identifiable from a Combination of Observations and Experiments NeurIPS 2023 Estimating Joint Treatment Effects by Combining Multiple Experiments ICML 2023 On Measuring Causal Contributions via do-interventions ICML 2022 Double Machine Learning Density Estimation for Local Treatment Effects with Instruments NeurIPS 2021 Estimating Identifiable Causal Effects on Markov Equivalence Class through Double Machine Learning ICML 2021 Estimating Identifiable Causal Effects through Double Machine Learning AAAI 2021 Estimating Causal Effects Using Weighting-Based Estimators AAAI 2020 Learning Causal Effects via Weighted Empirical Risk Minimization NeurIPS 2020