Jin Tian

Causal Inference & Discovery · 2000–2019

A researcher in the Science corpus: 11 papers; trusts 23 researchers and is trusted by 56 researchers here, as inferred from the citation graph.

Subfields

• Causal Inference & Discovery — 37 citations into it; #95 of 364 by trust here, #124 by citations
• Statistics and Probability — 35 citations into it; #2,649 of 12,932 by trust here, #2,579 by citations
• Artificial Intelligence — 31 citations into it; #3,274 of 35,345 by trust here, #7,413 by citations
• Probabilistic & Bayesian Methods — 27 citations into it
• Statistical Learning Theory — 6 citations into it
• Evaluation & Benchmarks — 5 citations into it

Whom Jin Tian trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Judea Pearl
2. J. Gerring
3. Peter M. Bentler
4. Elias Bareinboim
5. James M. Robins
6. Joseph Y. Halpern
7. Tom L. Burr
8. Thomas Verma
9. Donald B. Rubin
10. James J. Heckman

Who trusts Jin Tian overall

1. Judea Pearl
2. Elias Bareinboim
3. Ilya Shpitser
4. Kun Zhang
5. Bernhard Schölkopf
6. Mathias Drton
7. Adarsh Subbaswamy
8. Juan David Correa
9. Biwei Huang
10. Florian Markowetz

Papers from Jin Tian's perspective

Ordered by how strongly the researchers Jin Tian trusts endorse each paper. Same corpus, a different order for every researcher.

1. Causality : Models , Reasoning , and Inference
2. Causation, Prediction, and Search
3. Equivalence and Synthesis of Causal Models
4. Causation
5. Probabilistic reasoning in intelligent systems - networks of plausible inference
6. Causal diagrams for empirical research
7. Estimating causal effects of treatments in randomized and nonrandomized studies.
8. Statistical Analysis With Missing Data
9. On the Testable Implications of Causal Models with Hidden Variables
10. Sample selection bias as a specification error

Their papers

• Causal Discovery from Changes (2001) — 182 citations
• Recovering from Selection Bias in Causal and Statistical Inference (2014) — 199 citations
• Bounds on Direct Effects in the Presence of Confounded Intermediate Variables (2008) — 111 citations
• On the Testable Implications of Causal Models with Hidden Variables (2002) — 192 citations
• Probabilities of causation: Bounds and identification (2000) — 264 citations
• Recovering Causal Effects from Selection Bias (2015) — 78 citations
• Generalized Adjustment Under Confounding and Selection Biases (2018) — 60 citations
• Adjustment Criteria for Generalizing Experimental Findings (2019) — 15 citations
• On the Identification of Causal Effects (2015) — 152 citations
• A general identification condition for causal effects (2002) — 420 citations
• Causal Discovery from Changes: a Bayesian Approach (2001) — 38 citations