Thomas S. Richardson
About
In The Last Decade
Thomas S. Richardson
61 papers receiving 1.7k citations
Peers
Comparison fields: 5 of 152
- Artificial Intelligence 880
- Statistics and Probability 794
- Molecular Biology 206
- Economics and Econometrics 162
- Management Science and Operations Research 159
Countries citing papers authored by Thomas S. Richardson
This map shows the geographic impact of Thomas S. Richardson'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 Thomas S. Richardson with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Thomas S. Richardson more than expected).
Fields of papers citing papers by Thomas S. Richardson
This network shows the impact of papers produced by Thomas S. Richardson. 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 Thomas S. Richardson. The network helps show where Thomas S. Richardson may publish in the future.
Co-authorship network of co-authors of Thomas S. Richardson
This figure shows the co-authorship network connecting the top 25 collaborators of Thomas S. Richardson. A scholar is included among the top collaborators of Thomas S. Richardson 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 Thomas S. Richardson. Thomas S. Richardson 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 | A finite population likelihood ratio test of the sharp null hypothesis for compliers | 0 |
| 3 | 4 | |
| 4 | Estimating the effect of joint interventions from observational data in sparse high-dimensional settings | 28 |
| 5 | Sparse nested Markov models with log-linear parameters | 1 |
| 6 | 15 | |
| 7 | Learning high-dimensional DAGs with latent and selection variables | 3 |
| 8 | An efficient algorithm for computing interventional distributions in latent variable causal models | 7 |
| 9 | 34 | |
| 10 | Testing edges by truncations | 8 |
| 11 | Beyond Bisection: Eigenvector-Based Partitioning of Networks into Multiple Communities | 1 |
| 12 | 14 | |
| 13 | 150 | |
| 14 | 74 | |
| 15 | 18 | |
| 16 | Markov equivalence classes for maximal ancestral graphs | 1 |
| 17 | Using the structure of d-connecting paths as a qualitative measure of the strength of dependence | 2 |
| 18 | Boosting methodology for regression problems. | 68 |
| 19 | Interpretable boosted naïve Bayes classification | 47 |
| 20 | 12 |
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.