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Journal of Causal Inference

Edited by: Kosuke Imai, Judea Pearl, Maya Liv Petersen, and Mark J. van der Laan
Your benefits
  • NO submission fees
  • VOLUNTARY publication fees
  • Interdisciplinary approach
  • Quantitative methodology
  • Outstanding editorial board
  • One of leading journals in causal inference
  • Open access publication
  • liberal policies on copyrights (authors retain copyrights) and on self-archiving
    (no embargo periods)
  • long-term preservation – content archiving with Portico

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.

Any field aiming at understanding causality, especially
  • Biostatistics and epidemiology
  • Economics
  • Political science
  • Public policy
  • Cognitive science
  • Formal logic
Causal inference:
  • Research design
  • Causal model and target parameter specification
  • Identifiability
  • Statistical estimation
  • Sensitivity analysis/interpretation.
  • 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 (ewa.piechota@degruyter.com). For more details please refer to Article Processing Charges document (in PDF).

Journal Metrics

Cite Score
Impact Factor
SCImago Journal Rank
Source Normalized Impact per Paper

Abstracting & Indexing

Journal of Causal Inference is covered by the following services:

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Submit your article through our Online Submission Tool

Your benefits of publishing with us

Submission process
  • Get familiar and set your manuscript according to our guidelines
  • Submission of your paper via Online Submission Tool
  • Peer review process
  • Decision on your paper
  • Online publishing
  • You will be guided through the whole process of submission
  • In case of any problems editorial assistance will be provided

Please note

We look forward to receiving your manuscript!

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




All those interested in the causal inference, especially researchers