P. M. Aronow
Statistics and Probability · 2012–2020
A researcher in the Science corpus: 13 papers; trusts 41 researchers and is trusted by 39 researchers here, as inferred from the citation graph.
Subfields
- Statistics and Probability — 107 citations into it; #930 of 12,932 by trust here, #1,662 by citations
- Economics and Econometrics — 43 citations into it; #13,647 of 23,481 by trust here, #22,128 by citations
- Sociology and Political Science — 42 citations into it
- Political Science and International Relations — 26 citations into it
- Safety Research — 18 citations into it
- Causal Inference & Discovery — 15 citations into it
- Education — 11 citations into it; #1,116 of 11,309 by trust here, #5,377 by citations
- Gender Studies — 11 citations into it
Whom P. M. Aronow trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Donald B. Rubin
- David Freedman
- Guido W. Imbens
- W. Lin
- Joshua D. Angrist
- Paul R. Rosenbaum
- E. Paluck
- Tyler J. VanderWeele
- Peter Z. Schochet
- Cyrus Samii
Who trusts P. M. Aronow overall
- Peng Ding
- Susan Athey
- Kosuke Imai
- Dean Eckles
- Guido W. Imbens
- Xinran Li
- Fredrik Savje
- Stefan Wager
- Guillaume W. Basse
- Avi Feller
Papers from P. M. Aronow's perspective
Ordered by how strongly the researchers P. M. Aronow trusts endorse each paper. Same corpus, a different order for every researcher.
- Estimating causal effects of treatments in randomized and nonrandomized studies.
- [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
- Statistics and Causal Inference
- Identification of Causal effects Using Instrumental Variables
- The central role of the propensity score in observational studies for causal effects
- Observational Studies
- On regression adjustments to experimental data
- The Design of Experiments
- Making the Most out of Programme Evaluations and Social Experiments: Accounting for Heterogeneity in Programme Impacts
- A Generalization of Sampling Without Replacement from a Finite Universe
Their papers
- A Class of Unbiased Estimators of the Average Treatment Effect in Randomized Experiments (2013) — 95 citations
- Estimating Average Causal Effects Under Interference Between Units (2013) — 251 citations
- Does Regression Produce Representative Estimates of Causal Effects (2016) — 166 citations
- On equivalencies between design-based and regression-based variance estimators for randomized experiments (2012) — 64 citations
- SHARP BOUNDS ON THE VARIANCE IN RANDOMIZED EXPERIMENTS (2014) — 88 citations
- A General Method for Detecting Interference Between Units in Randomized Experiments (2012) — 113 citations
- Unbiased Estimation of the Average Treatment Effect in Cluster-Randomized Experiments (2015) — 85 citations
- Cluster–Robust Variance Estimation for Dyadic Data (2013) — 126 citations
- Field Experiments and the Study of Voter Turnout (2013) — 208 citations
- The Book of Why: The New Science of Cause and Effect (2020) — 132 citations
- Irregularities in LaCour (2014) (2020)
- A Note on Dropping Experimental Subjects who Fail a Manipulation Check (2019) — 240 citations
- Estimating Average Causal Effects Under General Interference (2012) — 330 citations