W. Shadish

Statistics and Probability · 2001–2014

A researcher in the Science corpus: 10 papers; trusts 57 researchers and is trusted by 49 researchers here, as inferred from the citation graph.

Subfields

• Statistics and Probability — 72 citations into it; #1,752 of 12,932 by trust here, #1,112 by citations
• Developmental and Educational Psychology — 21 citations into it; #2,141 of 8,223 by trust here, #2,262 by citations
• Economics and Econometrics — 18 citations into it; #15,466 of 23,481 by trust here, #23,209 by citations
• Causal Inference & Discovery — 14 citations into it
• Education — 9 citations into it; #2,221 of 11,309 by trust here, #2,294 by citations
• Artificial Intelligence — 8 citations into it
• Gender Studies — 8 citations into it
• Management Science and Operations Research — 8 citations into it; #4,274 of 11,946 by trust here, #6,024 by citations

Whom W. Shadish trusts overall

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

1. Donald B. Rubin
2. Kosuke Imai
3. James M. Robins
4. Eric R. Ziegel
5. T. D. Cook
6. Richard I. Parker
7. Paul R. Rosenbaum
8. Keying Ye
9. Guido W. Imbens
10. Roderick J. A. Little

Who trusts W. Shadish overall

1. J. Smith
2. M. H. Clark
3. David Weisburd
4. Elias Bareinboim
5. Elizabeth Tipton
6. T. Lund
7. Sally A. Lesik
8. Paul M. Leonardi
9. John Antonakis
10. Thomas Kratochwill

Papers from W. Shadish's perspective

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

1. Statistics and Causal Inference
2. The central role of the propensity score in observational studies for causal effects
3. Observational Studies
4. Estimating causal effects of treatments in randomized and nonrandomized studies.
5. Marginal Structural Models and Causal Inference in Epidemiology
6. Full Matching in an Observational Study of Coaching for the SAT
7. [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
8. Matching As An Econometric Evaluation Estimator
9. Efficient estimation of average treatment effects using the estimated propensity score
10. Does Matching Overcome Lalonde's Critique of Nonexperimental Estimators?

Their papers

• Propensity Scores (2005) — 338 citations
• Three conditions under which experiments and observational studies produce comparable causal estimates: New findings from within‐study comparisons (2008) — 654 citations
• Can Nonrandomized Experiments Yield Accurate Answers? A Randomized Experiment Comparing Random and Nonrandom Assignments (2008) — 544 citations
• The state of the science in the meta-analysis of single-case experimental designs (2008) — 190 citations
• Statistical Analyses of Single-Case Designs (2014) — 87 citations
• A Primer on Propensity Score Analysis (2010) — 111 citations
• Design-Comparable Effect Sizes in Multiple Baseline Designs (2014) — 202 citations
• Single-Case Intervention Research Design Standards (2013) — 1,333 citations
• On the Importance of Reliable Covariate Measurement in Selection Bias Adjustments Using Propensity Scores (2011) — 154 citations
• Experimental and Quasi-Experimental Designs for Generalized Causal Inference (2001) — 13,809 citations