Andrew Forney
Impact in
- Statistics and Probability top 5%
- Advanced Causal Inference Techniques
- Statistical Methods and Inference
Papers in
-
- Reinforcement Learning in Robotics 2
- Adversarial Robustness in Machine Learning 1
-
- Advanced Causal Inference Techniques 3
- Statistical Methods and Inference 2
- Statistics Education and Methodologies 1
- Co-authors
- Judea Pearl (4 shared papers)Carlos Cinelli (2 shared papers)Elias Bareinboim (3 shared papers)Richard Gilbert (1 shared paper)
- Journals
- Political Science Quarterly (1 paper)International Journal of Human-Computer Studies (1 paper)Sociological Methods & Research (1 paper)neural information processing systems (1 paper)DOAJ (DOAJ: Directory of Open Access Journals) (1 paper)
- Partner nations
- United States
In The Last Decade
Andrew Forney
8 papers receiving 431 citations
Andrew Forney's Hit Papers
Peers
Comparison fields: 5 of 116
- Statistics and Probability 65
- General Decision Sciences 9
- Economics and Econometrics 84
- Management Science and Operations Research 38
- Artificial Intelligence 72
Countries citing papers authored by Andrew Forney
This map shows the geographic impact of Andrew Forney's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Andrew Forney with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Andrew Forney more than expected).
Fields of papers citing papers by Andrew Forney
This network shows the impact of papers produced by Andrew Forney. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Andrew Forney. The network helps show where Andrew Forney may publish in the future.
Co-authors
The 4 scholars most cited alongside Andrew Forney, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | A Crash Course in Good and Bad Controls Hit paper breakdown → | 2022 | 286 |
| 2 | 2020 | 76 | |
| 3 | Bandits with unobserved confounders: a causal approach | 2015 | 34 |
| 4 | Counterfactual Data-Fusion for Online Reinforcement Learners. | 2017 | 18 |
| 5 | 2014 | 14 | |
| 6 | 2019 | 6 | |
| 7 | 2022 | 5 | |
| 8 | 2016 | 1 | |
| 9 | 2022 | 0 | |
| 10 | 2020 | 0 |
About Andrew Forney
Andrew Forney is a scholar working on Artificial Intelligence, Statistics and Probability, Management Science and Operations Research, Communication and Political Science and International Relations, having authored 10 papers that have together received 440 indexed citations. Recurring topics across this work include Advanced Causal Inference Techniques (3 papers), Advanced Bandit Algorithms Research (3 papers), Reinforcement Learning in Robotics (2 papers), Statistical Methods and Inference (2 papers), Statistics Education and Methodologies (1 paper), Face Recognition and Perception (1 paper), Psychology of Moral and Emotional Judgment (1 paper) and Adversarial Robustness in Machine Learning (1 paper). The work is most often cited by research in Statistics and Probability (65 citations), General Decision Sciences (9 citations), Economics and Econometrics (84 citations), Management Science and Operations Research (38 citations) and Artificial Intelligence (72 citations). Andrew Forney has collaborated with scholars based in United States. Frequent co-authors include Judea Pearl, Carlos Cinelli, Elias Bareinboim and Richard Gilbert. Their work appears in journals such as Political Science Quarterly, International Journal of Human-Computer Studies, Sociological Methods & Research, neural information processing systems and DOAJ (DOAJ: Directory of Open Access Journals).
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.