Andrew Forney
Impact in
- Statistics and Probability top 5%
- Advanced Causal Inference Techniques
- Statistical Methods and Inference
Papers in
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- Reinforcement Learning in Robotics 2
- Bayesian Modeling and Causal Inference 1
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- Advanced Causal Inference Techniques 3
- Statistical Methods and Inference 2
- Statistical Methods and Bayesian Inference 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)SHILAP Revista de lepidopterología (1 paper)Proceedings of the AAAI Conference on Artificial Intelligence (1 paper)
- Partner nations
- United States
In The Last Decade
Andrew Forney
8 papers receiving 393 citations
Hit Papers
Peers
Comparison fields: 5 of 118
- Statistics and Probability 58
- General Decision Sciences 9
- Economics and Econometrics 79
- Management Science and Operations Research 34
- Applied Psychology 12
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 | 254 |
| 2 | 2020 | 75 | |
| 3 | Bandits with unobserved confounders: a causal approach | 2015 | 33 |
| 4 | Counterfactual Data-Fusion for Online Reinforcement Learners. | 2017 | 17 |
| 5 | 2014 | 13 | |
| 6 | 2019 | 6 | |
| 7 | 2022 | 4 | |
| 8 | 2016 | 1 |
About Andrew Forney
Andrew Forney is a scholar working on Artificial Intelligence, Statistics and Probability, Management Science and Operations Research, Political Science and International Relations and Social Psychology, having authored 8 papers that have together received 403 indexed citations. Recurring topics across this work include Advanced Causal Inference Techniques (3 papers), Advanced Bandit Algorithms Research (2 papers), Statistical Methods and Inference (2 papers), Reinforcement Learning in Robotics (2 papers), Statistical Methods and Bayesian Inference (1 paper), Action Observation and Synchronization (1 paper), Bayesian Modeling and Causal Inference (1 paper) and Health Systems, Economic Evaluations, Quality of Life (1 paper). The work is most often cited by research in Statistics and Probability (58 citations), General Decision Sciences (9 citations), Economics and Econometrics (79 citations), Management Science and Operations Research (34 citations) and Applied Psychology (12 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, SHILAP Revista de lepidopterología and Proceedings of the AAAI Conference on Artificial Intelligence.
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.