Razieh Nabi
Artificial Intelligence · 2017–2020
A researcher in the Science corpus: 3 papers; trusts 34 researchers and is trusted by 38 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 33 citations into it
- Statistics and Probability — 27 citations into it; #1,998 of 12,932 by trust here, #2,407 by citations
- Safety, Robustness & Interpretability — 18 citations into it
- Causal Inference & Discovery — 15 citations into it
- AI Ethics, Fairness & Society — 11 citations into it; #47 of 494 by trust here, #97 by citations
- Probabilistic & Bayesian Methods — 10 citations into it
- Safety Research — 8 citations into it
- Management Science and Operations Research — 6 citations into it
Whom Razieh Nabi trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Judea Pearl
- James M. Robins
- Ilya Shpitser
- E. T. Tchetgen Tchetgen
- Thomas S. Richardson
- Jin Tian
- Tyler J. VanderWeele
- Tom L. Burr
- Dino Pedreschi
- Lu Zhang
Who trusts Razieh Nabi overall
- Simon Caton
- Ninareh Mehrabi
- Lu Zhang
- Sahil Verma
- Berk Ustun
- Joshua R. Loftus
- Silvia Chiappa
- Julius von Kügelgen
- Yongkai Wu
- David Madras
Papers from Razieh Nabi's perspective
Ordered by how strongly the researchers Razieh Nabi trusts endorse each paper. Same corpus, a different order for every researcher.
- Causality : Models , Reasoning , and Inference
- Direct and Indirect Effects
- Causation, Prediction, and Search
- Equivalence and Synthesis of Causal Models
- Identification of Joint Interventional Distributions in Recursive Semi-Markovian Causal Models
- A new approach to causal inference in mortality studies with a sustained exposure period—application to control of the healthy worker survivor effect
- On the Testable Implications of Causal Models with Hidden Variables
- Identifiability and Exchangeability for Direct and Indirect Effects
- Equality of Opportunity in Supervised Learning
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
Their papers
- Fair Inference on Outcomes (2017) — 389 citations
- Learning Optimal Fair Policies (2018) — 107 citations
- A Semiparametric Approach to Interpretable Machine Learning (2020) — 6 citations