Shu Yang

Statistics and Probability · Journal of the American Statistical Association · 2015–2019

A researcher in the Science corpus: 6 papers; trusts 65 researchers and is trusted by 12 researchers here, as inferred from the citation graph.

Subfields

• Statistics and Probability — 98 citations into it; #4,455 of 12,932 by trust here, #6,853 by citations
• Causal Inference & Discovery — 31 citations into it
• Economics and Econometrics — 21 citations into it
• Artificial Intelligence — 8 citations into it; #12,553 of 35,345 by trust here, #33,294 by citations
• Management Science and Operations Research — 7 citations into it
• Education — 5 citations into it
• Probabilistic & Bayesian Methods — 5 citations into it

Whom Shu Yang trusts overall

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

1. Guido W. Imbens
2. Donald B. Rubin
3. Jinyong Hahn
4. Phillip S. Kott
5. Keisuke Hirano
6. A. P. Dawid
7. J. Sanchis-Llopis
8. Susanne M. Schennach
9. J. Michael Oakes
10. Kosuke Imai

Who trusts Shu Yang overall

1. Tarid Wongvorachan
2. Ingo Zettler
3. Surina He
4. Okan Bulut
5. Christoph Schild
6. Xinwei Ma
7. Lau Lilleholt

Papers from Shu Yang's perspective

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

1. The central role of the propensity score in observational studies for causal effects
2. Statistics and Causal Inference
3. Estimating causal effects of treatments in randomized and nonrandomized studies.
4. [On the Application of Probability Theory to Agricultural Experiments. Essay on Principles. Section 9.] Comment: Neyman (1923) and Causal Inference in Experiments and Observational Studies
5. INFERENCE AND MISSING DATA
6. Efficient estimation of average treatment effects using the estimated propensity score
7. Doubly Robust Estimation in Missing Data and Causal Inference Models
8. Estimation of Regression Coefficients When Some Regressors are not Always Observed
9. On the Role of the Propensity Score in Efficient Semiparametric Estimation of Average Treatment Effects
10. Observational Studies

Their papers

• Propensity score matching and subclassification in observational studies with multi‐level treatments (2015) — 138 citations
• Flexible Imputation of Missing Data, 2nd ed. (2019) — 527 citations
• Asymptotic inference of causal effects with observational studies trimmed by the estimated propensity scores (2018) — 94 citations
• Multi-cause causal inference with unmeasured confounding and binary outcome. (2019) — 8 citations
• Causal inference with confounders missing not at random (2017) — 76 citations
• Propensity Score Weighting for Causal Inference with Clustered Data (2017) — 49 citations