Madelyn Glymour
- Artificial Intelligence top 2%
- Statistics and Probability top 2%
- Computer Vision and Pattern Recognition top 10%
- Cognitive Neuroscience
- Management Science and Operations Research top 5%
- Co-authors
- Nicholas P. JewellJudea PearlClark GlymourJoseph RamseyRubén Sánchez-RomeroBiwei HuangKun Zhang
- Topics
- Functional Brain Connectivity Studies (2 papers)Statistical Methods and Inference (2 papers)Bayesian Modeling and Causal Inference (2 papers)
- Cited by
- Statistics and ProbabilityArtificial IntelligenceManagement Science and Operations Research
- Journals
- The British Journal for the Philosophy of ScienceNetwork NeuroscienceInternational Journal of Data Science and Analytics
- Partner nations
- United States
In The Last Decade
Madelyn Glymour
4 papers receiving 1.1k citations
Hit Papers
Peers
Comparison fields: 5 of 147
- Artificial Intelligence 480
- Statistics and Probability 183
- Computer Vision and Pattern Recognition 134
- Cognitive Neuroscience 101
- Management Science and Operations Research 97
Countries citing papers authored by Madelyn Glymour
This map shows the geographic impact of Madelyn Glymour'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 Madelyn Glymour with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Madelyn Glymour more than expected).
Fields of papers citing papers by Madelyn Glymour
This network shows the impact of papers produced by Madelyn Glymour. 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 Madelyn Glymour. The network helps show where Madelyn Glymour may publish in the future.
Co-authorship network of co-authors of Madelyn Glymour
This figure shows the co-authorship network connecting the top 25 collaborators of Madelyn Glymour. A scholar is included among the top collaborators of Madelyn Glymour based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Madelyn Glymour. Madelyn Glymour is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 49 | |
| 2 | 0 | |
| 3 | Causal inference in statistics : a primerbreakdown → | 798 |
| 4 | 178 | |
| 5 | Causal Inference in Statistics | 63 |
About Madelyn Glymour
Madelyn Glymour is a scholar working on Statistics and Probability, Cognitive Neuroscience and Artificial Intelligence, having authored 5 papers that have together received 1.1k indexed citations. Recurring topics across this work include Functional Brain Connectivity Studies (2 papers), Statistical Methods and Inference (2 papers) and Bayesian Modeling and Causal Inference (2 papers). The work is most often cited by research in Statistics and Probability (183 citations), Artificial Intelligence (480 citations) and Management Science and Operations Research (97 citations). Madelyn Glymour has collaborated with scholars based in United States. Frequent co-authors include Nicholas P. Jewell, Judea Pearl, Clark Glymour, Joseph Ramsey, Rubén Sánchez-Romero, Biwei Huang, Kun Zhang and Kun Zhang. Their work appears in journals such as The British Journal for the Philosophy of Science, Network Neuroscience and International Journal of Data Science and Analytics.
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.