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Application Deadline: May 25, 2023
You can find more details on responsibilities and how to apply in the EiC recruitment file in Supplementary Materials.
Neyman (1923) and its influences on causal inference
COORDINATING EDITOR: Peng Ding (University of California, USA)
Deadline for submissions: October 1, 2023
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
- Computer Science
- Machine Learning
- Political science
- Public policy
- Cognitive science
- Formal logic
- Research design
- Causal model and target parameter specification
- 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
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 is subject to an Article Processing Charge. The standard APC amounts to €1000. However, covering this fee is not mandatory and depends on the authors’ financial ability. The Journal offers extensive DISCOUNT and WAIVER policy, so we strongly encourage the authors to contact us and discuss their willingness to cover the APC (or part of it). There are no submission charges – Article Processing Charges apply after the acceptance of a manuscript. For more details, please refer to the Article Processing Charges document (in PDF).