Alexander Volfovsky

Statistics and Probability · 2017–2021

A researcher in the Science corpus: 9 papers; trusts 53 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Statistics and Probability — 97 citations into it; #4,866 of 12,932 by trust here, #5,661 by citations
• Causal Inference & Discovery — 26 citations into it; #176 of 364 by trust here, #295 by citations
• Artificial Intelligence — 19 citations into it; #18,681 of 35,345 by trust here, #28,481 by citations
• Economics and Econometrics — 16 citations into it
• Education — 12 citations into it
• Sociology and Political Science — 12 citations into it
• Probabilistic & Bayesian Methods — 11 citations into it
• Statistical Learning Theory — 6 citations into it

Whom Alexander Volfovsky trusts overall

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

1. Donald B. Rubin
2. José R. Zubizarreta
3. Paul R. Rosenbaum
4. Cynthia Rudin
5. Guido W. Imbens
6. Max H. Farrell
7. Ben B. Hansen
8. Elizabeth A. Stuart
9. Sudeepa Roy
10. Alberto Abadie

Who trusts Alexander Volfovsky overall

1. Sudeepa Roy
2. Cynthia Rudin
3. Harsh Parikh
4. M. Usaid Awan
5. Marco Morucci

Papers from Alexander Volfovsky's perspective

Ordered by how strongly the researchers Alexander Volfovsky 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. Estimating causal effects of treatments in randomized and nonrandomized studies.
3. Large Sample Properties of Matching Estimators for Average Treatment Effects
4. ImageNet classification with deep convolutional neural networks
5. Matching methods for causal inference: A review and a look forward
6. Regression Shrinkage and Selection via the Lasso
7. Evaluating the Econometric Evaluations of Training Programs with Experimental Data
8. Identification of Causal effects Using Instrumental Variables
9. Controlling Bias in Observational Studies: A Review.
10. Design Of Observational Studies

Their papers

• FLAME: A Fast Large-scale Almost Matching Exactly Approach to Causal Inference (2017) — 54 citations
• Exposure to opposing views on social media can increase political polarization (2018) — 1,554 citations
• An Application of Matching After Learning To Stretch (MALTS) to the ACIC 2018 Causal Inference Challenge Data (2021)
• Adaptive Hyper-box Matching for Interpretable Individualized Treatment Effect Estimation (2020) — 16 citations
• Almost-Matching-Exactly for Treatment Effect Estimation under Network Interference (2020) — 15 citations
• MALTS: Matching After Learning to Stretch (2018) — 30 citations
• dame-flame: A Python Library Providing Fast Interpretable Matching for Causal Inference (2021) — 4 citations
• Interpretable Almost-Exact Matching for Causal Inference (2019) — 25 citations
• Interpretable Almost Matching Exactly With Instrumental Variables (2019) — 5 citations