Jin Tian
36 papers · 2009–2026 · 7 venues · across top CS/AI venues
This profile combines several different researchers. Authors are keyed by name, and this name's papers fall into 12 separate collaboration groups - the largest holds only 42% of them. Pick the group below that matches the researcher you mean. How a shared name is split.
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Which Jin Tian?
4 groups of papers that share co-authors internally but none with each other - each most likely a different researcher. Pick one to see its papers.
with Elias Bareinboim, Yonghan Jung, Hyunchai Jeong
15 papers
· 2019–2025
· AAAI, NeurIPS, ICML
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Testing Causal Models with Hidden Variables in Polynomial Delay via Conditional Independencies
AAAI 2025
Unified Covariate Adjustment for Causal Inference
NeurIPS 2024
Causal Effect Identification in Cluster DAGs
AAAI 2023
Estimating Causal Effects Identifiable from a Combination of Observations and Experiments
NeurIPS 2023
Estimating Joint Treatment Effects by Combining Multiple Experiments
ICML 2023
Finding and Listing Front-door Adjustment Sets
NeurIPS 2022
On Measuring Causal Contributions via do-interventions
ICML 2022
Partial Counterfactual Identification from Observational and Experimental Data
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
Adjustment Criteria for Generalizing Experimental Findings
ICML 2019
Identification of Causal Effects in the Presence of Selection Bias
AAAI 2019
8 papers whose co-authors appear nowhere else under this name
Causal Logistic Bandits with Counterfactual Fairness Constraints
ICML 2025
Neuron Dependency Graphs: A Causal Abstraction of Neural Networks
ICML 2022
A Mutual Information Regularization for Adversarial Training
ACML 2021
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
Graphical Models for Inference with Missing Data
NeurIPS 2013
Markov Properties for Linear Causal Models with Correlated Errors
JMLR 2009