D. Westreich

Statistics and Probability · 2010–2017

A researcher in the Science corpus: 7 papers; trusts 25 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Statistics and Probability — 61 citations into it; #6,957 of 12,932 by trust here, #2,153 by citations
• Causal Inference & Discovery — 17 citations into it
• Artificial Intelligence — 12 citations into it
• Economics and Econometrics — 12 citations into it; #20,364 of 23,481 by trust here, #19,308 by citations

Whom D. Westreich trusts overall

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

1. James M. Robins
2. Donald B. Rubin
3. Greg Ridgeway
4. Sander Greenland
5. Judea Pearl
6. Paul R. Rosenbaum
7. Miguel A. Hern'an
8. Stephen R. Cole
9. Ji Zhu
10. Tyler J. VanderWeele

Who trusts D. Westreich overall

1. Irina Degtiar
2. O. Efthimiou
3. Wentao Bao
4. M. J. Funk
5. Paul H unermund
6. Sherri Rose
7. Michael Seo
8. Uri Shalit
9. H. Kim
10. Andrew Bennett

Papers from D. Westreich's perspective

Ordered by how strongly the researchers D. Westreich 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. [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
3. Estimating causal effects of treatments in randomized and nonrandomized studies.
4. Generalizing evidence from randomized clinical trials to target populations: The ACTG 320 trial.
5. Doubly Robust Estimation in Missing Data and Causal Inference Models
6. Confounding and Collapsibility in Causal Inference
7. Estimating causal effects from epidemiological data (Journal of Epidemiology and Community Health)
8. Statistics and Causal Inference
9. Demystifying Double Robustness: A Comparison of Alternative Strategies for Estimating a Population Mean from Incomplete Data
10. Observational Studies

Their papers

• All your data are always missing: incorporating bias due to measurement error into the potential outcomes framework. (2015) — 77 citations
• The table 2 fallacy: presenting and interpreting confounder and modifier coefficients. (2013) — 1,042 citations
• Propensity score estimation: machine learning and classification methods as alternatives to logistic regression (2010) — 479 citations
• Illustrating bias due to conditioning on a collider. (2010) — 791 citations
• Doubly robust estimation of causal effects. (2011) — 1,135 citations
• Transportability of Trial Results Using Inverse Odds of Sampling Weights. (2017) — 298 citations
• Incomprehensible Discrimination (2017)