Peng Ding

Statistics and Probability · Journal of the American Statistical Association · 2011–2023

A researcher in the Science corpus: 20 papers; trusts 138 researchers and is trusted by 27 researchers here, as inferred from the citation graph.

Subfields

• Statistics and Probability — 270 citations into it; #1,644 of 12,932 by trust here, #1,262 by citations
• Economics and Econometrics — 53 citations into it; #12,764 of 23,481 by trust here, #9,444 by citations
• Causal Inference & Discovery — 48 citations into it
• Management Science and Operations Research — 29 citations into it
• Artificial Intelligence — 27 citations into it; #14,188 of 35,345 by trust here, #25,809 by citations
• Sociology and Political Science — 12 citations into it
• Probabilistic & Bayesian Methods — 10 citations into it
• Economics & Operations Research — 7 citations into it

Whom Peng Ding trusts overall

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

1. Donald B. Rubin
2. David Freedman
3. Guido W. Imbens
4. Paul R. Rosenbaum
5. W. Lin
6. Philippe Huber
7. P. M. Aronow
8. Joshua D. Angrist
9. Alberto Abadie
10. Halbert White

Who trusts Peng Ding overall

1. Xinran Li
2. Tyler J. VanderWeele
3. Kevin Guo
4. Jonathan Roth
5. C. Fogarty
6. Bin Yu
7. Stefan Wager
8. Anqi Zhao
9. Jason A. Wu
10. Zhichao Jiang

Papers from Peng Ding's perspective

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

1. [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
2. Estimating causal effects of treatments in randomized and nonrandomized studies.
3. Statistics and Causal Inference
4. The central role of the propensity score in observational studies for causal effects
5. On regression adjustments to experimental data
6. Observational Studies
7. INFERENCE AND MISSING DATA
8. Making the Most out of Programme Evaluations and Social Experiments: Accounting for Heterogeneity in Programme Impacts
9. Agnostic notes on regression adjustments to experimental data: Reexamining Freedman's critique
10. Regression Shrinkage and Selection via the Lasso

Their papers

• Decomposing Treatment Effect Variation (2016) — 98 citations
• A paradox from randomization-based causal inference (2014) — 81 citations
• Overlap in observational studies with high-dimensional covariates (2017) — 229 citations
• Sensitivity Analysis Without Assumptions (2015) — 529 citations
• General Forms of Finite Population Central Limit Theorems with Applications to Causal Inference (2016) — 210 citations
• Rerandomization and regression adjustment (2019) — 115 citations
• Sensitivity Analysis in Observational Research: Introducing the E-Value (2017) — 4,720 citations
• Covariate-adjusted Fisher randomization tests for the average treatment effect (2020) — 48 citations
• A Bracketing Relationship between Difference-in-Differences and Lagged-Dependent-Variable Adjustment (2019) — 78 citations
• Identifiability and Estimation of Causal Effects by Principal Stratification With Outcomes Truncated by Death (2011) — 103 citations
• Asymptotic inference of causal effects with observational studies trimmed by the estimated propensity scores (2018) — 94 citations
• Principal causal effect identification and surrogate end point evaluation by multiple trials (2015) — 33 citations
• A First Course in Causal Inference (2023) — 77 citations
• Technical Considerations in the Use of the E-Value (2019) — 92 citations
• Randomization Tests for Weak Null Hypotheses in Randomized Experiments (2018) — 80 citations
• Causal inference with confounders missing not at random (2017) — 76 citations
• Bridging Finite and Super Population Causal Inference (2017) — 50 citations
• On the Conditional Distribution of the Multivariate t Distribution (2016) — 96 citations
• Principal stratification analysis using principal scores (2016) — 147 citations
• Identifiability of Normal and Normal Mixture Models with Nonignorable Missing Data (2015) — 122 citations