Alexander D’Amour
- Economics and Econometrics top 5%
- Safety Research top 2%
- Artificial Intelligence top 5%
- Strategy and Management top 10%
- Management of Technology and Innovation top 5%
- Topics
- Advanced Causal Inference Techniques (5 papers)Statistical Methods and Inference (4 papers)Statistical Methods and Bayesian Inference (3 papers)
- Partner nations
- United StatesFranceUnited Kingdom
In The Last Decade
Alexander D’Amour
19 papers receiving 1.0k citations
Hit Papers
Peers
Comparison fields: 5 of 111
- Economics and Econometrics 303
- Safety Research 224
- Artificial Intelligence 217
- Strategy and Management 141
- Management of Technology and Innovation 133
Countries citing papers authored by Alexander D’Amour
This map shows the geographic impact of Alexander D’Amour'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 Alexander D’Amour with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Alexander D’Amour more than expected).
Fields of papers citing papers by Alexander D’Amour
This network shows the impact of papers produced by Alexander D’Amour. 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 Alexander D’Amour. The network helps show where Alexander D’Amour may publish in the future.
Co-authorship network of co-authors of Alexander D’Amour
This figure shows the co-authorship network connecting the top 25 collaborators of Alexander D’Amour. A scholar is included among the top collaborators of Alexander D’Amour 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 Alexander D’Amour. Alexander D’Amour is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 6 | |
| 3 | 2 | |
| 4 | 0 | |
| 5 | 3 | |
| 6 | 15 | |
| 7 | Counterfactual Invariance to Spurious Correlations in Text Classification | 13 |
| 8 | Algorithmic Fairness: Choices, Assumptions, and Definitionsbreakdown → | 286 |
| 9 | 85 | |
| 10 | 55 | |
| 11 | 45 | |
| 12 | On Multi-Cause Approaches to Causal Inference with Unobserved Counfounding: Two Cautionary Failure Cases and A Promising Alternative | 14 |
| 13 | Universal Causal Evaluation Engine: An API for empirically evaluating causal inference models | 4 |
| 14 | 3 | |
| 15 | 2 | |
| 16 | 11 | |
| 17 | 28 | |
| 18 | Disambiguation and co-authorship networks of the U.S. patent inventor database (1975–2010)breakdown → | 385 |
| 19 | 72 | |
| 20 | 5 |
About Alexander D’Amour
Alexander D’Amour is a scholar working on Statistics and Probability, Health Informatics and Statistics, Probability and Uncertainty, having authored 21 papers that have together received 1.0k indexed citations. Recurring topics across this work include Advanced Causal Inference Techniques (5 papers), Statistical Methods and Inference (4 papers) and Statistical Methods and Bayesian Inference (3 papers). The work is most often cited by research in Health Informatics (49 citations), Safety Research (224 citations) and Management of Technology and Innovation (133 citations). Alexander D’Amour has collaborated with scholars based in United States, France and United Kingdom. Frequent co-authors include Shira Mitchell, Kristian Lum, Eric Potash, Solon Barocas, Lee Fleming, Amy Yu, Ronald Lai, Ye Sun, Vetle I. Torvik and David M. Doolin. Their work appears in journals such as Journal of the American Statistical Association, Research Policy and Journal of Econometrics.
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.