Instrumental variable methods for causal inference

450 indexed citations

Abstract

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About

This paper, published in 2014, received 450 indexed citations. Written by Michael Baiocchi, Jing Cheng and Dylan S. Small covering the research area of Statistics and Probability and Artificial Intelligence. It is primarily cited by scholars working on Statistics and Probability (186 citations), Economics and Econometrics (129 citations) and General Health Professions (44 citations). Published in Statistics in Medicine.

In The Last Decade

doi.org/10.1002/sim.6128 →

Countries where authors are citing Instrumental variable methods for causal inference

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This map shows the geographic impact of Instrumental variable methods for causal inference. 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 Instrumental variable methods for causal inference with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Instrumental variable methods for causal inference more than expected).

Fields of papers citing Instrumental variable methods for causal inference

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Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of Instrumental variable methods for causal inference. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Instrumental variable methods for causal inference.

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/10.1002/sim.6128.

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