Sudeepa Roy

Statistics and Probability · 2017–2021

A researcher in the Science corpus: 6 papers; trusts 47 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Statistics and Probability — 79 citations into it; #5,727 of 12,932 by trust here, #7,165 by citations
• Causal Inference & Discovery — 21 citations into it; #200 of 364 by trust here, #327 by citations
• Artificial Intelligence — 15 citations into it; #17,524 of 35,345 by trust here, #28,481 by citations
• Economics and Econometrics — 13 citations into it
• Education — 11 citations into it
• Probabilistic & Bayesian Methods — 8 citations into it

Whom Sudeepa Roy trusts overall

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

1. Donald B. Rubin
2. José R. Zubizarreta
3. Cynthia Rudin
4. Paul R. Rosenbaum
5. Marco Morucci
6. Stefano Maria Iacus
7. Hugh A. Chipman
8. A. Kondracki
9. Jinyong Hahn
10. Harsh Parikh

Who trusts Sudeepa Roy overall

1. M. Usaid Awan
2. Cynthia Rudin
3. Harsh Parikh
4. Alexander Volfovsky
5. Marco Morucci
6. Yameng Liu
7. Awa Dieng
8. Vittorio Orlandi

Papers from Sudeepa Roy's perspective

Ordered by how strongly the researchers Sudeepa Roy 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. ImageNet classification with deep convolutional neural networks
4. Regression Shrinkage and Selection via the Lasso
5. Matching methods for causal inference: A review and a look forward
6. Identification of Causal effects Using Instrumental Variables
7. Evaluating the Econometric Evaluations of Training Programs with Experimental Data
8. Estimation and Inference of Heterogeneous Treatment Effects using Random Forests
9. Bayesian Nonparametric Modeling for Causal Inference
10. Econometric Analysis of Cross Section and Panel Data

Their papers

• FLAME: A Fast Large-scale Almost Matching Exactly Approach to Causal Inference (2017) — 54 citations
• 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
• 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