Jin Tian

33 papers · 2009–2026 · 7 conferences · across top CS/AI conferences

Achievements

+12 more ↓

🌍 Conference Polyglot (7) 🐣 Hot Topic Early Bird 🌉 Interdisciplinary Bridge 🧭 Keyword Pioneer 🏃 Academic Marathon (16)

🧭 Keyword Pioneer 🐣 Hot Topic Early Bird 🤝 Dynamic Duo (15) 🔬 Deep Specialist (22) 🧬 Topic Evolution 🏆 Keyword Champion (5) 🚀 Conference Pioneer ⚡ Prolific Year (6) 🗃️ Keyword Collector (107) 💎 Century Club (32) 📈 Trend Setter 🔥 Unstoppable (7)

Conferences

ICML (8) AAAI (7) NIPS (6) UAI (4) ACML (3) AISTATS (3) JMLR (2)

Top co-authors

Elias Bareinboim (15) Yonghan Jung (9) Yuta Kawakami (7) manabu kuroki (3) Hyunchai Jeong (2) Yetian Chen (2) Yanpeng Zhao (1) Patrick Bloebaum (1) Christopher John Quinn (1) Changsung Kang (1)

Research topics

Statistics (1)

Keywords

causal inference (17) causal effect (5) causal effect estimation (5) graphical model (4) bayesian network (4) double machine learning (3) interventional distribution (2) structure learning (2) missing datum (2) directed acyclic graph (2) structural causal model (2) causal identification (2) probabilistic modeling (2) instrumental variable (2) observational datum (2) conditional independence (2) causal effect identification (2) causal graph (2) influence function (2) causal diagram (2)

Papers

Potential Outcome Rankings for Counterfactual Decision Making AAAI 2026 Testing Causal Models with Hidden Variables in Polynomial Delay via Conditional Independencies AAAI 2025 Causal Logistic Bandits with Counterfactual Fairness Constraints ICML 2025 Graph-based Complexity for Causal Effect by Empirical Plug-in AISTATS 2025 Mediation Analysis for Probabilities of Causation AAAI 2025 Decomposition of Probabilities of Causation with Two Mediators UAI 2025 Moments of Causal Effects UAI 2025 Probabilities of Causation for Continuous and Vector Variables UAI 2024 Unified Covariate Adjustment for Causal Inference NIPS 2024 Identification and Estimation of Conditional Average Partial Causal Effects via Instrumental Variable UAI 2024 Causal Effect Identification in Cluster DAGs AAAI 2023 Estimating Causal Effects Identifiable from a Combination of Observations and Experiments NIPS 2023 Instrumental Variable Estimation of Average Partial Causal Effects ICML 2023 Estimating Joint Treatment Effects by Combining Multiple Experiments ICML 2023 Partial Counterfactual Identification from Observational and Experimental Data ICML 2022 On Measuring Causal Contributions via do-interventions ICML 2022 Neuron Dependency Graphs: A Causal Abstraction of Neural Networks ICML 2022 Finding and Listing Front-door Adjustment Sets NIPS 2022 A Mutual Information Regularization for Adversarial Training ACML 2021 Estimating Identifiable Causal Effects through Double Machine Learning AAAI 2021 Double Machine Learning Density Estimation for Local Treatment Effects with Instruments NIPS 2021 Estimating Identifiable Causal Effects on Markov Equivalence Class through Double Machine Learning ICML 2021 Learning Causal Effects via Weighted Empirical Risk Minimization NIPS 2020 Estimating Causal Effects Using Weighting-Based Estimators AAAI 2020 Adjustment Criteria for Generalizing Experimental Findings ICML 2019 Identification of Causal Effects in the Presence of Selection Bias AAAI 2019 Recovering Probability Distributions from Missing Data ACML 2017 Structure Learning in Bayesian Networks of a Moderate Size by Efficient Sampling JMLR 2016 Missing at Random in Graphical Models AISTATS 2015 Curriculum Learning of Bayesian Network Structures ACML 2015 Exact Bayesian Learning of Ancestor Relations in Bayesian Networks AISTATS 2015 Graphical Models for Inference with Missing Data NIPS 2013 Markov Properties for Linear Causal Models with Correlated Errors JMLR 2009