Test Cover Image of:  Journal of Causal Inference
Impact Factor
1.720

Journal of Causal Inference

Edited by: Kosuke Imai, Judea Pearl, Maya Liv Petersen, Jasjeet Sekhon and Mark J. van der Laan
Your benefits
  • Interdisciplinary approach
  • Quantitative methodology
  • Outstanding editorial board
  • One of leading journals in causal inference
  • Open access publication

Objective
Journal of Causal Inference (JCI) is a fully peer-reviewed, open access, electronic-only journal.

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
  • 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

Journal Metrics

Cite Score
1.0
Impact Factor
1.720
SCImago Journal Rank
0.709
Source Normalized Impact per Paper
0.457

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Volume 8 (2020): Issue 1 (Jan 2020)

Submission
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!

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
Jasjeet Sekhon, University of California, Berkeley, 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
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
Miguel Hernan, Harvard School of Public Health, 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

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Audience:

All those interested in the causal inference, especially researchers