Harsh Parikh

Statistics and Probability · 2018–2021

A researcher in the Science corpus: 2 papers; trusts 24 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Statistics and Probability — 24 citations into it; #7,184 of 12,932 by trust here, #9,714 by citations
• Causal Inference & Discovery — 7 citations into it; #256 of 364 by trust here, #361 by citations
• Artificial Intelligence — 5 citations into it

Whom Harsh Parikh 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. Jerome H. Friedman
5. R. Lalonde
6. Elizabeth A. Stuart
7. Ben B. Hansen
8. Max H. Farrell
9. Guido W. Imbens
10. Sudeepa Roy

Who trusts Harsh Parikh overall

1. Sudeepa Roy
2. Cynthia Rudin
3. Alexander Volfovsky

Papers from Harsh Parikh's perspective

Ordered by how strongly the researchers Harsh Parikh 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. ImageNet classification with deep convolutional neural networks
3. Regression Shrinkage and Selection via the Lasso
4. Large Sample Properties of Matching Estimators for Average Treatment Effects
5. Matching methods for causal inference: A review and a look forward
6. Evaluating the Econometric Evaluations of Training Programs with Experimental Data
7. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
8. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
9. Controlling Bias in Observational Studies: A Review.
10. Gradient-based learning applied to document recognition

Their papers

• An Application of Matching After Learning To Stretch (MALTS) to the ACIC 2018 Causal Inference Challenge Data (2021)
• MALTS: Matching After Learning to Stretch (2018) — 30 citations