The Landscape of Causal Inference: Perspective From Citation Network Analysis

Weihua An and Ying Ding

The American Statistician, 2018, vol. 72, issue 3, 265-277

Abstract: Causal inference is a fast-growing multidisciplinary field that has drawn extensive interests from statistical sciences and health and social sciences. In this article, we gather comprehensive information on publications and citations in causal inference and provide a review of the field from the perspective of citation network analysis. We provide descriptive analyses by showing the most cited publications, the most prolific and the most cited authors, and structural properties of the citation network. Then, we examine the citation network through exponential random graph models (ERGMs). We show that both technical aspects of the publications (e.g., publication length, time and quality) and social processes such as homophily (the tendency to cite publications in the same field or with shared authors), cumulative advantage, and transitivity (the tendency to cite references’ references), matter for citations. We also provide specific analysis of citations among the top authors in the field and present a ranking and clustering of the authors. Overall, our article reveals new insights into the landscape of the field of causal inference and may serve as a case study for analyzing citation networks in a multidisciplinary field and for fitting ERGMs on big networks. Supplementary materials for this article are available online.

Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
http://hdl.handle.net/10.1080/00031305.2017.1360794 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:amstat:v:72:y:2018:i:3:p:265-277

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/UTAS20

DOI: 10.1080/00031305.2017.1360794

Access Statistics for this article

The American Statistician is currently edited by Eric Sampson

More articles in The American Statistician from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().