Causal inference in statistics: An overview
Impact in
Classified as
- Authors
- Judea Pearl
- Journal
- Project Euclid (Cornell University)
In The Last Decade
doi.org/10.1214/09-ss057 →Countries where authors are citing Causal inference in statistics: An overview
This map shows the geographic impact of Causal inference in statistics: An overview. 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 in statistics: An overview 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 in statistics: An overview more than expected).
Fields of papers citing Causal inference in statistics: An overview
This network shows the impact of Causal inference in statistics: An overview. 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 in statistics: An overview.
About Causal inference in statistics: An overview
This paper, published in 2009, received 1.3k indexed citations . Written by Judea Pearl covering the research area of Artificial Intelligence and Statistics and Probability. It is primarily cited by scholars working on Artificial Intelligence (405 citations), Statistics and Probability (249 citations), Economics and Econometrics (124 citations), Management Science and Operations Research (112 citations) and Sociology and Political Science (102 citations). Published in Project Euclid (Cornell University).
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/10.1214/09-ss057.