← Authors

Jin Tian

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

Achievements

🌍 Conference Polyglot (7) πŸ—ΊοΈ Taxonomy Completionist (28) 🐝 Cross-Pollinator (13) πŸŒ‰ Interdisciplinary Bridge πŸƒ Academic Marathon (16) 🧭 Keyword Pioneer 🐣 Hot Topic Early Bird 🀝 Dynamic Duo (15) πŸ”¬ Deep Specialist (22) 🧬 Topic Evolution πŸ† Keyword Champion (5) πŸš€ Conference Pioneer ⚑ Prolific Year (6) πŸ—ƒοΈ Keyword Collector (107) πŸ’Ž Century Club (32) πŸ“ˆ Trend Setter πŸ”₯ 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