Andrew Goodman-Bacon

Statistics and Probability · 2018–2021

A researcher in the Science corpus: 3 papers; trusts 25 researchers and is trusted by 33 researchers here, as inferred from the citation graph.

Subfields

• Statistics and Probability — 37 citations into it; #1,067 of 12,932 by trust here, #2,751 by citations
• Economics and Econometrics — 35 citations into it; #1,641 of 23,481 by trust here, #7,584 by citations
• Sociology and Political Science — 12 citations into it
• Gender Studies — 7 citations into it
• Causal Inference & Discovery — 6 citations into it
• Education — 5 citations into it

Whom Andrew Goodman-Bacon trusts overall

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

1. Joshua D. Angrist
2. A. Cameron
3. Alberto Abadie
4. Jeffrey M. Wooldridge
5. Brantly Callaway
6. Pravin K. Trivedi
7. S. Abraham
8. Donald B. Rubin
9. R. D. Blander
10. C. Chaisemartin

Who trusts Andrew Goodman-Bacon overall

1. C. Chaisemartin
2. J. Gardner
3. X. d'Haultfoeuille
4. Cristina Peñasco
5. Elena Prager
6. Brantly Callaway
7. Jonathan Roth
8. Erik Brynjolfsson
9. Teevrat Garg
10. Pedro H. C. Sant’Anna

Papers from Andrew Goodman-Bacon's perspective

Ordered by how strongly the researchers Andrew Goodman-Bacon trusts endorse each paper. Same corpus, a different order for every researcher.

1. Empirical Strategies in Labor Economics
2. Statistics and Causal Inference
3. Estimating dynamic treatment effects in event studies with heterogeneous treatment effects (Journal of Econometrics)
4. Semiparametric instrumental variable estimation of treatment response models
5. Mostly Harmless Econometrics: An Empiricist's Companion
6. Revisiting event study designs: robust and efficient estimation
7. Mostly Harmless Econometrics: An Empiricist's Companion
8. [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
9. Solutions Manual and Supplementary Materials for Econometric Analysis of Cross Section and Panel Data
10. The central role of the propensity score in observational studies for causal effects

Their papers

• Difference-in-differences with variation in treatment timing (2018) — 6,271 citations
• Difference-in-Differences with a Continuous Treatment (2021) — 361 citations
• Using Difference-in-Differences to Identify Causal Effects of COVID-19 Policies (2020) — 207 citations