Jann Spiess

Statistics and Probability · 2017–2021

A researcher in the Science corpus: 2 papers; trusts 19 researchers and is trusted by 23 researchers here, as inferred from the citation graph.

Subfields

• Statistics and Probability — 39 citations into it; #3,447 of 12,932 by trust here, #2,800 by citations
• Economics and Econometrics — 36 citations into it; #9,260 of 23,481 by trust here, #16,151 by citations
• Sociology and Political Science — 11 citations into it
• Causal Inference & Discovery — 10 citations into it
• Artificial Intelligence — 9 citations into it
• Management Science and Operations Research — 9 citations into it; #2,456 of 11,946 by trust here, #2,355 by citations
• Economics & Operations Research — 6 citations into it; #61 of 244 by trust here, #54 by citations
• Finance — 6 citations into it

Whom Jann Spiess trusts overall

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

1. Susan Athey
2. Guido W. Imbens
3. Joshua D. Angrist
4. Alberto Abadie
5. Kosuke Imai
6. Donald B. Rubin
7. C. Chaisemartin
8. Victor Chernozhukov
9. Christian Hansen
10. Paulo Guimarães

Who trusts Jann Spiess overall

1. Susan Athey
2. Perry Sadorsky
3. C. Chaisemartin
4. Markku V. J. Maula
5. Michael Greenstone
6. A. Fagereng
7. Erik Brynjolfsson
8. Michael C. Bailey
9. Rohit Sharma
10. X. d'Haultfoeuille

Papers from Jann Spiess's perspective

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

1. Recursive partitioning for heterogeneous causal effects
2. Split-Sample Instrumental Variables Estimates of the Return to Schooling
3. On Model Selection Consistency of Lasso
4. ALTERNATIVE APPROXIMATIONS TO THE DISTRIBUTIONS OF INSTRUMENTAL VARIABLE ESTIMATORS
5. Big Data: New Tricks for Econometrics
6. Instrumental Variables Regression with Weak Instruments
7. A regularization approach to the many instruments problem
8. Design-based Analysis in Difference-In-Differences Settings with Staggered Adoption
9. CAN ONE ESTIMATE THE UNCONDITIONAL DISTRIBUTION OF POST-MODEL-SELECTION ESTIMATORS? (Econometric Theory)
10. A Practical Guide to Counterfactual Estimators for Causal Inference with Time-Series Cross-Sectional Data (Social Science Research Network)

Their papers

• Machine Learning: An Applied Econometric Approach (2017) — 1,678 citations
• Revisiting event study designs: robust and efficient estimation (2021) — 1,690 citations