liberal policies on copyrights (authors retain copyrights) and on self-archiving (no embargo periods)
long-term preservation – content archiving with Portico
Objective Journal of Causal Inference (JCI) is a fully peer-reviewed, open access, electronic-only journal. The journal provides the readers with free, instant, and permanent access to all content worldwide; and the authors with extensive promotion of published articles, long-term preservation, no space constraints.
JCI publishes papers on theoretical and applied causal research across the range of academic disciplines that use quantitative tools to study causality.
The past two decades have seen causal inference emerge as a unified field with a solid theoretical foundation, useful in many of the empirical and behavioral sciences. Journal of Causal Inference aims to provide a common venue for researchers working on causal inference in biostatistics and epidemiology, economics, political science and public policy, cognitive science and formal logic, and any field that aims to understand causality. The journal serves as a forum for this growing community to develop a shared language and study the commonalities and distinct strengths of their various disciplines' methods for causal analysis.
Existing discipline-specific journals tend to bury causal analysis in the language and methods of traditional statistical methodologies, creating the inaccurate impression that causal questions can be handled by routine methods of regression or simultaneous equations, glossing over the special precautions demanded by causal analysis. In contrast, JCI highlights both the uniqueness and interdisciplinary nature of causal research.
Topics Any field aiming at understanding causality, especially
Biostatistics and epidemiology
Causal model and target parameter specification
Quantitative statistics’ elaboration of causal methods in applied data analyses
Cross-disciplinary methodological research
History of the causal inference field and its philosophical underpinnings
Article formats Original research articles, book reviews, short communications on topics that aim to stimulate public debate and bring unorthodox perspectives to open questions
Open Access model Due to the switch to Open Access model beginning from 2020 the Journal of Causal Inference will be subject to an volountary Article Processing Charge (APC). There will be NO submission charges – Article Processing Charges will apply after the acceptance of a manuscript. Authors, who have limited access to funds, may request for a discount or full waiver. Inquiries concerning APCs should be addressed before or immediately after submission of a paper to the Managing Editor (email@example.com). For more details please refer to Article Processing Charges document (in PDF).
SCImago Journal Rank
Source Normalized Impact per Paper
Abstracting & Indexing
Journal of Causal Inference is covered by the following services:
CNKI Scholar (China National Knowledge Infrastructure)
CNPIEC - cnpLINKer
DOAJ (Directory of Open Access Journals)
EBSCO (relevant databases)
EBSCO Discovery Service
Japan Science and Technology Agency (JST)
Journal Citation Reports/Social Sciences Edition
KESLI-NDSL (Korean National Discovery for Science Leaders)
Norwegian Register for Scientific Journals, Series and Publishers
Primo Central (ExLibris)
QOAM (Quality Open Access Market)
Research Papers in Economics (RePEc)
Ulrich's Periodicals Directory/ulrichsweb
Web of Science - Current Contents/Social and Behavioral Sciences
Editors Kosuke Imai, Harvard University, USA Judea Pearl, University of California, Los Angeles, USA Maya Petersen, University of California, Berkeley School of Public Health, USA Mark van der Laan, University of California, Berkeley School of Public Health, USA
Editorial Board Alberto Abadie, Harvard University, USA Jaap H. Abbring, Tilburg University, Netherlands Peter Aronow, Yale University, USA Laura B. Balzer, University of Massachusetts – Amherst, USA Elias Bareinboim, Columbia University, USA David Benkeser, Emory University, USA Kenneth Bollen, University of North Carolina, USA Marco Carone, University of Washington, USA Matias D. Cattaneo, Princeton University, USA Antoine Chambaz, Université Paris Ouest Nanterre, France Philip Dawid, University of Cambridge, UK Peng Ding, University of California, Berkeley, USA Felix Elwert, University of Wisconsin-Madison, USA Avi Feller, University of California, Berkeley, USA Donald Green, Columbia University, USA Sander Greenland, University of California, Los Angeles, USA Jens Hainmueller, Stanford University, USA Joseph Halpern, Cornell University, USA James Heckman, University of Chicago, USA Jennifer Hill, New York University, USA Christopher Hitchcock, California Institute of Technology, USA Paul Hünermund, School of Business and Economics, Maastricht University, The Netherlands Marshall Joffe, University of Pennsylvania, USA Cheng Ju, University of California, Berkeley, USA Luke Keele, Penn State University, USA Manabu Kuroki, The Institute of Statistical Mathematics, Tokyo, Japan Edward Miguel, University of California, Berkeley, USA Karthika Mohan, University of California, Berkeley, USA Romain Neugebauer, Kaiser Permanente Michael Oakes, University of Minnesota School of Public Health, USA Sam Pimentel, University of California, Berkeley, USA Ed Rigdon, Georgia State University, USA James Robins, Harvard School of Public Health, USA Michael Rosenblum, Johns Hopkins Bloomberg School of Public Health, USA Andrea Rotnitsky, Harvard School of Public Health, USA Ilya Shpitser, University of Southampton, UK Dylan Small, The Wharton School, University of Pennsylvania, USA Michael Sobel, Columbia University, USA Peter Sprites, Carnegie Mellon University, USA Elizabeth Stuart, Johns Hopkins University, USA Eric Tchetgen Tchetgen, Harvard School of Public Health, USA Jin Tian, Iowa State University, USA Tyler VanderWeele, Harvard School of Public Health, USA Stijn Vansteelandt, Ghent University, Belgium, and London School of Public Health, UK Ed Vytlacil, Yale University, USA Steven West, Arizona State University, USA Christopher Winship, Harvard University, USA Teppei Yamamoto, Massachusetts Institute of Technology, USA
01 Jun 2013
2 issues per year
All those interested in the causal inference, especially researchers