Impact Factor: 1.4

Open Access Published since June 1, 2013

Journal of Causal Inference

ISSN: 2193-3685

Editors-in-chief: Elias Bareinboim, Iván Díaz, Jin Tian

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About this journal

Journal of Causal Inference is a fully peer-reviewed, open access, electronic journal that provides readers with free, instant, and permanent access to all content worldwide.

Aims and Scope

Journal of Causal Inference 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

Economics

Machine Learning

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

Journal of Causal Inference accepts submissions of research articles, reviews, and communications. Submission of a manuscript implies that the work described is not copyrighted, published, or submitted elsewhere, except in abstract form. The corresponding author should ensure that all authors approve the manuscript before submission.

Your Benefits

As an author of Journal of Causal Inference, You benefit from

fair and constructive peer review

quick online publication of accepted papers (continuous publication model)

language polishing services upon acceptance for authors from non-English speaking regions

no limitations on colour figures and word count in published articles

all articles are freely available to the academic community worldwide without any restrictions

promotion of published papers to readers and citers

distribution to open access directories (such as DOAJ) and thousands of libraries worldwide

liberal policies on copyrights (authors retain copyright) and on self-archiving (no embargo periods) (License to Publish (CC-BY))

secure archiving by De Gruyter and the independent archiving service Portico

Article Processing Charges

In order to sustain the publishing process, each article accepted for publication in Journal of Causal Inference is subject to an Article Processing Charge of €1000. This fee is used to cover the costs of the peer-review process, professional typesetting and copyediting, as well as online hosting, long-term preservation, and extensive promotion to potential readers. There is no submission fee. Information regarding payment of the charge will be provided following acceptance for publication. For more information please go to Article Processing Charges .

Publication Ethics and Editorial Policies

Detailed information on Editorial Policy, Publication Ethics, Instructions for Authors etc. can be found in the Supplementary Materials section.

For more information on De Gruyter Publishing Ethics, please see the De Gruyter Guidelines online here.

Special Issues

Special Issue based on the 2023 Interactive Causal Learning Conference
COORDINATING EDITORS: Carlos Cinelli (University of Washington, USA), Michael Ryall (Florida Atlantic University, USA), Kevin Bryan (University of Toronto, Canada)
DEADLINE FOR SUBMISSIONS: May 31, 2024

More information soon!

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Supplementary Materials

Topics

External Links

Volume 12 Issue 1

January 2024

Research Articles

Open Access January 12, 2024

Evaluating Boolean relationships in Configurational Comparative Methods

Luna De Souter

Article number: 20230014

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Abstract

Configurational Comparative Methods (CCMs) aim to learn causal structures from datasets by exploiting Boolean sufficiency and necessity relationships. One important challenge for these methods is that such Boolean relationships are often not satisfied in real-life datasets, as these datasets usually contain noise. Hence, CCMs infer models that only approximately fit the data, introducing a risk of inferring incorrect or incomplete models, especially when data are also fragmented (have limited empirical diversity). To minimize this risk, evaluation measures for sufficiency and necessity should be sensitive to all relevant evidence. This article points out that the standard evaluation measures in CCMs, consistency and coverage, neglect certain evidence for these Boolean relationships. Correspondingly, two new measures, contrapositive consistency and contrapositive coverage, which are equivalent to the binary classification measures specificity and negative predictive value, respectively, are introduced to the CCM context as additions to consistency and coverage. A simulation experiment demonstrates that the introduced contrapositive measures indeed help to identify correct CCM models.

Open Access February 2, 2024

Doubly weighted M-estimation for nonrandom assignment and missing outcomes

Akanksha Negi

Article number: 20230016

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Abstract

This article proposes a class of M-estimators that double weight for the joint problems of nonrandom treatment assignment and missing outcomes. Identification of the main parameter of interest is achieved under unconfoundedness and missing at random assumptions with respect to the treatment and sample selection problems, respectively. Given the parametric framework, the asymptotic theory of the proposed estimator is outlined in two parts: first, when the parameter solves an unconditional problem, and second, when it solves a stronger conditional problem. The two parts help to summarize the misspecification scenarios permissible under the given framework and the role played by double weighting in each. As illustrative examples, the article also discusses the estimation of causal parameters like average and quantile treatment effects. With respect to the average treatment effect, this article shows that the proposed estimator is doubly robust . Finally, a detailed application to Calónico and Smith’s (The women of the national supported work demonstration. J Labor Econom. 2017;35(S1):S65–S97.) reconstructed sample from the National Supported Work training program is used to demonstrate the estimator’s performance in empirical settings.

Open Access February 22, 2024

Regression(s) discontinuity: Using bootstrap aggregation to yield estimates of RD treatment effects

Mark C. Long, Jordan Rooklyn

Article number: 20220028

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Abstract

Following Efron (2014), we propose an algorithm for estimating treatment effects for use by researchers employing a regression-discontinuity (RD) design. This algorithm generates a set of estimates of the treatment effect from bootstrapped samples, wherein the polynomial-selection algorithm developed by Pei, Lee, Card, and Weber (2021) is applied to each sample, the average of these RD treatment effect (RDTE) estimates is computed and serves as the overall estimate of the RDTE. Effectively, this procedure estimates a set of plausible RD estimates and weights the estimates by their likelihood of being the best estimate to form a weighted-average estimate. We discuss why this procedure may lower the estimate’s root mean squared error (RMSE). In simulation results, we show that this better performance is achieved, yielding up to a 5% reduction in RMSE relative to PLCW’s method and a 16% reduction in RMSE relative to Calonico, Cattaneo, and Titiunik’s (2014) method for bandwidth selection (with default settings).

Open Access April 24, 2024

Energy balancing of covariate distributions

Jared D. Huling, Simon Mak

Article number: 20220029

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Abstract

Bias in causal comparisons has a correspondence with distributional imbalance of covariates between treatment groups. Weighting strategies such as inverse propensity score weighting attempt to mitigate bias by either modeling the treatment assignment mechanism or balancing specified covariate moments. This article introduces a new weighting method, called energy balancing, which instead aims to balance weighted covariate distributions. By directly targeting distributional imbalance, the proposed weighting strategy can be flexibly utilized in a wide variety of causal analyses without the need for careful model or moment specification. Our energy balancing weights (EBW) approach has several advantages over existing weighting techniques. First, it offers a model-free and robust approach for obtaining covariate balance that does not require tuning parameters, obviating the need for modeling decisions of secondary nature to the scientific question at hand. Second, since this approach is based on a genuine measure of distributional balance, it provides a means for assessing the balance induced by a given set of weights for a given dataset. We demonstrate the effectiveness of this EBW approach in a suite of simulation experiments, and in studies on the safety of right heart catheterization and on three additional studies using electronic health record data.

Open Access April 24, 2024

A phenomenological account for causality in terms of elementary actions

Dominik Janzing, Sergio Hernan Garrido Mejia

Article number: 20220076

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Abstract

Discussions on causal relations in real life often consider variables for which the definition of causality is unclear since the notion of interventions on the respective variables is obscure. Asking “what qualifies an action for being an intervention on the variable X X ” raises the question whether the action impacted all other variables only through X or directly , which implicitly refers to a causal model. To avoid this known circularity, we instead suggest a notion of “phenomenological causality” whose basic concept is a set of elementary actions . Then the causal structure is defined such that elementary actions change only the causal mechanism at one node (e.g. one of the causal conditionals in the Markov factorization). This way, the principle of independent mechanisms becomes the defining property of causal structure in domains where causality is a more abstract phenomenon rather than being an objective fact relying on hard-wired causal links between tangible objects. In other words, causal relations between variables get defined by the interface between the system and an external agent (who is able to perform the elementary actions), rather than being an internal property of links between the variables. We describe this phenomenological approach to causality for toy and hypothetical real-world examples and argue that it is consistent with the causal Markov condition when the system under consideration interacts with other variables that control the elementary actions.

Open Access April 24, 2024

Nonparametric estimation of conditional incremental effects

Alec McClean, Zach Branson, Edward H. Kennedy

Article number: 20230024

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Abstract

Conditional effect estimation has great scientific and policy importance because interventions may impact subjects differently depending on their characteristics. Most research has focused on estimating the conditional average treatment effect (CATE). However, identification of the CATE requires that all subjects have a non-zero probability of receiving treatment, or positivity, which may be unrealistic in practice. Instead, we propose conditional effects based on incremental propensity score interventions, which are stochastic interventions where the odds of treatment are multiplied by some factor. These effects do not require positivity for identification and can be better suited for modeling scenarios in which people cannot be forced into treatment. We develop a projection approach and a flexible nonparametric estimator that can each estimate all the conditional effects we propose and derive model-agnostic error guarantees showing that both estimators satisfy a form of double robustness. Further, we propose a summary of treatment effect heterogeneity and a test for any effect heterogeneity based on the variance of a conditional derivative effect and derive a nonparametric estimator that also satisfies a form of double robustness. Finally, we demonstrate our estimators by analyzing the effect of intensive care unit admission on mortality using a dataset from the (SPOT)light study.

Review Article

Open Access January 10, 2024

Comparison of open-source software for producing directed acyclic graphs

Amy J. Pitts, Charlotte R. Fowler

Article number: 20230031

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Abstract

Many software packages have been developed to assist researchers in drawing directed acyclic graphs (DAGs), each with unique functionality and usability. We examine five of the most common software to generate DAGs: Ti k Z, DAGitty, ggdag, dagR, and igraph. For each package, we provide a general description of its background, analysis and visualization capabilities, and user-friendliness. In addition, in order to compare packages, we produce two DAGs in each software, the first featuring a simple confounding structure and the second with a more complex structure with three confounders and a mediator. We provide recommendations for when to use each software depending on the user’s needs.

Special Issue on Neyman (1923) and its influences on causal inference

Open Access February 8, 2024

Optimal allocation of sample size for randomization-based inference from 2^K factorial designs

Arun Ravichandran, Nicole E. Pashley, Brian Libgober, Tirthankar Dasgupta

Article number: 20230046

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Abstract

Optimizing the allocation of units into treatment groups can help researchers improve the precision of causal estimators and decrease costs when running factorial experiments. However, existing optimal allocation results typically assume a super-population model and that the outcome data come from a known family of distributions. Instead, we focus on randomization-based causal inference for the finite-population setting, which does not require model specifications for the data or sampling assumptions. We propose exact theoretical solutions for optimal allocation in 2 K {2}^{K} factorial experiments under complete randomization with A-, D-, and E-optimality criteria. We then extend this work to factorial designs with block randomization. We also derive results for optimal allocations when using cost-based constraints. To connect our theory to practice, we provide convenient integer-constrained programming solutions using a greedy optimization approach to find integer optimal allocation solutions for both complete and block randomizations. The proposed methods are demonstrated using two real-life factorial experiments conducted by social scientists.

Volume 7 (2019)

Volume 6 (2018)

Volume 5 (2017)

Volume 4 (2016)

Volume 3 (2015)

Volume 2 (2014)

Volume 1 (2013)

Journal Impact Factor	1.4	2022, Journal Citation Reports (Clarivate, 2023)
5-year Journal Impact Factor	3.7	2022, Journal Citation Reports (Clarivate, 2023)
Journal Citation Indicator	0.68	2022, Journal Citation Reports (Clarivate, 2023)
CiteScore	1.9	2022, Scopus (Elsevier B.V., 2023)
SCImago Journal Rank	0.934	2022, SJR (Scimago Lab, 2023; Data Source: Scopus)
Source Normalized Impact per Paper	1.612	2022, CWTS Journal Indicators (CWTS B.V., 2023; Data Source: Scopus)
Index Copernicus Value	158.67	2022, ICI Journals Master List (Index Copernicus, 2023)

More information about journal rankings

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MANUSCRIPTS
Journal of Causal Inference encourages the submission of substantial full-length bodies of work. There are no specific length restrictions for the overall manuscript or individual sections; however, we urge the authors to present and discuss their findings in a concise and accessible manner.

All submissions must be made via online submission system ScholarOne. In case of problems, please contact the Editorial Office of this journal (jci_editorial@degruyter.com).

SUBMISSION OF MANUSCRIPTS
Manuscripts have to be written in LATEX, AMS-TEX, AMS-LATEX. We do not accept papers in Plain TEX format. For an initial submission, the authors are strongly advised to upload their entire manuscript, including tables and figures, as a single PDF file. Authors are strongly advised to submit the final version of the paper using the journal’s LaTex Template.

For detailed information, please see Instruction for Authors

EDITORIAL POLICY
Unpublished material: Submission of a manuscript implies that the work described is not copyrighted, published or submitted elsewhere, except in abstract form. The corresponding author should ensure that all authors approve the manuscript before its submission.
Ethical conduct of research: The authors must describe and confirm safeguards to meet ethical standards when applicable. See Editorial Policy for details.
Conflict of interest: When authors submit a manuscript, they are responsible for recognizing and disclosing financial and/or other conflicts of interest that might bias their work and/or could inappropriately influence his/her judgment. If no specified acknowledgement is given, the Editors assume that no conflict of interest exists. Authors are encouraged to fill in the ICMJE Conflicts of Interest Form (available here) and send it in the electronic format to the Managing Editor.
Copyright: All authors retain copyright, unless – due to their local circumstances – their work is not copyrighted. The copyrights are governed by the Creative-Commons Attribution Only license (CC-BY) which is compliant with Plan-S. The corresponding author grants the journal the license to use of the article, by signing the License To Publish. Scanned copy of license should be sent to the journal, as soon as possible.
Authorship: Authorship should be limited to those who have made a significant contribution to the conception, design, execution, or interpretation of the reported study. All those who have made significant contributions should be listed as co-authors. Where there are others who have participated in certain substantive aspects of the research project, they should be named in an Acknowledgement section. The corresponding author should ensure that all appropriate co-authors (according to the above definition) and no inappropriate co-authors are included in the author list of the manuscript, and that all co-authors have seen and approved the final version of the paper and have agreed to its submission for publication.
Peer Review process: Our standard policy requires each paper reporting primary research or secondary analysis of primary research, together with relevant supplementary materials, to be reviewed by at least two Referees and the peer-review process is single-blind. The Editors reserve the right to decline the submitted manuscript without review, if the studies reported are not sufficiently novel or important to merit publication in the journal. Manuscripts deemed unsuitable (insufficient originality or of limited interest to the target audience) are returned to the author(s) without review. The Editor seeks advice from experts in the appropriate field. Research articles and communications are refereed by a minimum of two reviewers, review papers by at least three. Authors are requested to suggest persons competent to review their manuscript. However, please note that this will be treated only as a suggestion, and the final selection of reviewers is exclusively the Editor's decision. The final decision of acceptance in made by Managing Editor or, in case of conflict, by the Editor-in-Chief.
Scientific Misconduct: This journal publishes only original manuscripts that are not also published or going to be published elsewhere. Multiple submissions/publications, or redundant publications (re-packaging in different words of data already published by the same authors) will be rejected. If they are detected only after publication, the journal reserves the right to publish a Retraction Note. In each particular case Editors will follow COPE’s Core Practices and implement the advices.

Editorial

Editors
Elias Bareinboim, Columbia University, USA
Jin Tian, Iowa State University, USA
Iván Díaz, New York University, USA

Editorial Advisory Board
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, Massachusetts Institute of Technology, USA
Laura Balzer, University of Massachusetts, Amherst, USA
David Benkeser, Emory University, USA
Marco Carone, University of Washington, USA
Matias Cattaneo, Princeton University, USA
Antoine Chambaz, Université Paris Ouest Nanterre, France
Carlos Cinelli, University of Washington, USA
Rhian Daniel, Cardiff University, UK
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
Maria Glymour, University of California, San Francisco, USA
Joseph Halpern, Cornell University, USA
Larry Han, Harvard T.H. Chan School of Public Health, USA
Nima Hejazi, Harvard T.H. Chan School of Public Health, USA
Jennifer Hill, New York University, USA
Christopher Hitchcock, California Institute of Technology, USA
Paul Hünermund, Copenhagen Business School, Denmark
Cheng Ju, University of California Berkeley, USA
Luke Keele, Georgetown University, USA
Manabu Kuroki, The Institute of Statistical Mathematics, Tokyo, Japan
Karthika Mohan, Oregon State University, USA
Joris Mooij, University of Amsterdam, Netherlands
Romain Neugebauer, Kaiser Permanente, USA
Jonas Peters, ETH Zurich, Switzerland
Sam Pimentel, University of California, Berkeley, USA
Michael Rosenblum, Johns Hopkins University, USA
Helene Rytgaard, University of Copenhagen, Denmark
Ilya Shpitser, Johns Hopkins University, USA
Ricardo Silva, University College London, UK
Elizabeth Stuart, Johns Hopkins University, USA
Tyler VanderWeele, Harvard University, USA
Teppei Yamamoto, Massachusetts Institute of Technology, USA

Editorial Contact
Kumaran Rengaswamy
Assistant Managing Editor, Compuscript
journal.editor@degruyter.com

Managing Editor
Monika Maleszka
Monika.Maleszka@degruyter.com

(Deutsch)

Type: Journal
Language: English
Publisher: De Gruyter
First published: June 1, 2013
Publication Frequency: 1 Issue per Year
Audience: All those interested in the causal inference, especially researchers

Journal of Causal Inference

Evaluating Boolean relationships in Configurational Comparative Methods

Abstract

Doubly weighted M-estimation for nonrandom assignment and missing outcomes

Abstract

Regression(s) discontinuity: Using bootstrap aggregation to yield estimates of RD treatment effects

Abstract

Energy balancing of covariate distributions

Abstract

A phenomenological account for causality in terms of elementary actions

Abstract

Nonparametric estimation of conditional incremental effects

Abstract

Comparison of open-source software for producing directed acyclic graphs

Abstract

Optimal allocation of sample size for randomization-based inference from 2K factorial designs

Abstract

Optimal allocation of sample size for randomization-based inference from 2^K factorial designs