Causal inference and the data-fusion problem
Impact in
Classified as
- Authors
- Elias BareinboimJudea Pearl
In The Last Decade
doi.org/10.1073/pnas.1510507113 →Countries where authors are citing Causal inference and the data-fusion problem
This map shows the geographic impact of Causal inference and the data-fusion problem. 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 Causal inference and the data-fusion problem with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Causal inference and the data-fusion problem more than expected).
Fields of papers citing Causal inference and the data-fusion problem
This network shows the impact of Causal inference and the data-fusion problem. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Causal inference and the data-fusion problem.
About Causal inference and the data-fusion problem
This paper, published in 2016, received 351 indexed citations . Written by Elias Bareinboim and Judea Pearl covering the research area of Artificial Intelligence and Statistics and Probability. It is primarily cited by scholars working on Statistics and Probability (129 citations), Artificial Intelligence (110 citations), Economics and Econometrics (39 citations), Management Science and Operations Research (33 citations) and Sociology and Political Science (31 citations). Published in Proceedings of the National Academy of Sciences.
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.
This paper is also available at doi.org/10.1073/pnas.1510507113.