Open Access Published since June 1, 2013

Journal of Causal Inference

ISSN: 2193-3685

Edited by: Kosuke Imai, Judea Pearl, Maya Liv Petersen, Mark J. van der Laan

Impact Factor: 1.867

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Special Issue on Integration of Observational Studies with Randomized Trials

COORDINATING EDITORS:

Elias Bareinboim, Columbia University, USA
Mark van der Laan, University of California, USA

The deadline for submissions is December 15, 2022, but individual papers will be reviewed and published online on an ongoing basis.

For more details, please see the CFP Integration of Observational Studies with Randomized Trialsfile in Supplementary Materials below.

Highly Cited Articles:

Approximate Kernel-Based Conditional Independence Tests for Fast Non-Parametric Causal Discovery (Eric V. Strobl, Kun Zhang and Shyam Visweswaran)
The Inflation Technique for Causal Inference with Latent Variables(Elie Wolfe, Robert W. Spekkens　and　Tobias Fritz)
Technical Considerations in the Use of the E-Value(Tyler J. VanderWeele, Peng Ding and Maya Mathur)

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
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

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 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).

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

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Supplementary Materials

Topics

External Links

Volume 10 Issue 1

January 2022

Editorial

Open Access September 16, 2022

Causation and decision: On Dawid’s “Decision theoretic foundation of statistical causality”

Judea Pearl

Page range: 221-226

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Abstract

In a recent issue of this journal, Philip Dawid (2021) proposes a framework for causal inference that is based on statistical decision theory and that is, in many aspects, compatible with the familiar framework of causal graphs (e.g., Directed Acyclic Graphs (DAGs)). This editorial compares the methodological features of the two frameworks as well as their epistemological basis.

Research Articles

Open Access March 13, 2022

Simple yet sharp sensitivity analysis for unmeasured confounding

Jose M. Peña

Page range: 1-17

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Abstract

We present a method for assessing the sensitivity of the true causal effect to unmeasured confounding. The method requires the analyst to set two intuitive parameters. Otherwise, the method is assumption free. The method returns an interval that contains the true causal effect and whose bounds are arbitrarily sharp, i.e., practically attainable. We show experimentally that our bounds can be tighter than those obtained by the method of Ding and VanderWeele, which, moreover, requires to set one more parameter than our method. Finally, we extend our method to bound the natural direct and indirect effects when there are measured mediators and unmeasured exposure–outcome confounding.

Open Access March 19, 2022

Decomposition of the total effect for two mediators: A natural mediated interaction effect framework

Xin Gao, Li Li, Li Luo

Page range: 18-44

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Abstract

Mediation analysis has been used in many disciplines to explain the mechanism or process that underlies an observed relationship between an exposure variable and an outcome variable via the inclusion of mediators. Decompositions of the total effect (TE) of an exposure variable into effects characterizing mediation pathways and interactions have gained an increasing amount of interest in the last decade. In this work, we develop decompositions for scenarios where two mediators are causally sequential or non-sequential. Current developments in this area have primarily focused on either decompositions without interaction components or with interactions but assuming no causally sequential order between the mediators. We propose a new concept called natural mediated interaction (MI) effect that captures the two-way and three-way interactions for both scenarios and extends the two-way MIs in the literature. We develop a unified approach for decomposing the TE into the effects that are due to mediation only, interaction only, both mediation and interaction, neither mediation nor interaction within the counterfactual framework. Finally, we compare our proposed decomposition to an existing method in a non-sequential two-mediator scenario using simulated data, and illustrate the proposed decomposition for a sequential two-mediator scenario using a real data analysis.

Open Access May 6, 2022

Causal inference with imperfect instrumental variables

Nikolai Miklin, Mariami Gachechiladze, George Moreno, Rafael Chaves

Page range: 45-63

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Abstract

Instrumental variables allow for quantification of cause and effect relationships even in the absence of interventions. To achieve this, a number of causal assumptions must be met, the most important of which is the independence assumption, which states that the instrument and any confounding factor must be independent. However, if this independence condition is not met, can we still work with imperfect instrumental variables? Imperfect instruments can manifest themselves by violations of the instrumental inequalities that constrain the set of correlations in the scenario. In this article, we establish a quantitative relationship between such violations of instrumental inequalities and the minimal amount of measurement dependence required to explain them for the case of discrete observed variables. As a result, we provide adapted inequalities that are valid in the presence of a relaxed measurement dependence assumption in the instrumental scenario. This allows for the adaptation of existing and new lower bounds on the average causal effect for instrumental scenarios with binary outcomes. Finally, we discuss our findings in the context of quantum mechanics.

Open Access May 19, 2022

A unifying causal framework for analyzing dataset shift-stable learning algorithms

Adarsh Subbaswamy, Bryant Chen, Suchi Saria

Page range: 64-89

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Abstract

Recent interest in the external validity of prediction models (i.e., the problem of different train and test distributions, known as dataset shift ) has produced many methods for finding predictive distributions that are invariant to dataset shifts and can be used for prediction in new, unseen environments. However, these methods consider different types of shifts and have been developed under disparate frameworks, making it difficult to theoretically analyze how solutions differ with respect to stability and accuracy. Taking a causal graphical view, we use a flexible graphical representation to express various types of dataset shifts. Given a known graph of the data generating process, we show that all invariant distributions correspond to a causal hierarchy of graphical operators, which disable the edges in the graph that are responsible for the shifts. The hierarchy provides a common theoretical underpinning for understanding when and how stability to shifts can be achieved, and in what ways stable distributions can differ. We use it to establish conditions for minimax optimal performance across environments, and derive new algorithms that find optimal stable distributions. By using this new perspective, we empirically demonstrate that that there is a tradeoff between minimax and average performance.

Open Access May 25, 2022

The variance of causal effect estimators for binary v-structures

Jack Kuipers, Giusi Moffa

Page range: 90-105

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Abstract

Adjusting for covariates is a well-established method to estimate the total causal effect of an exposure variable on an outcome of interest. Depending on the causal structure of the mechanism under study, there may be different adjustment sets, equally valid from a theoretical perspective, leading to identical causal effects. However, in practice, with finite data, estimators built on different sets may display different precisions. To investigate the extent of this variability, we consider the simplest non-trivial non-linear model of a v-structure on three nodes for binary data. We explicitly compute and compare the variance of the two possible different causal estimators. Further, by going beyond leading-order asymptotics, we show that there are parameter regimes where the set with the asymptotically optimal variance does depend on the edge coefficients, a result that is not captured by the recent leading-order developments for general causal models. As a practical consequence, the adjustment set selection needs to account for the relative magnitude of the relationships between variables with respect to the sample size and cannot rely on purely graphical criteria.

Open Access May 31, 2022

Treatment effect optimisation in dynamic environments

Jeroen Berrevoets, Sam Verboven, Wouter Verbeke

Page range: 106-122

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Abstract

Applying causal methods to fields such as healthcare, marketing, and economics receives increasing interest. In particular, optimising the individual-treatment-effect – often referred to as uplift modelling – has peaked in areas such as precision medicine and targeted advertising. While existing techniques have proven useful in many settings, they suffer vividly in a dynamic environment. To address this issue, we propose a novel optimisation target that is easily incorporated in bandit algorithms. Incorporating this target creates a causal model which we name an uplifted contextual multi-armed bandit . Experiments on real and simulated data show the proposed method to effectively improve upon the state-of-the-art. All our code is made available online at https://github.com/vub-dl/u-cmab.

Open Access May 31, 2022

Optimal weighting for estimating generalized average treatment effects

Nathan Kallus, Michele Santacatterina

Page range: 123-140

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Abstract

In causal inference, a variety of causal effect estimands have been studied, including the sample, uncensored, target, conditional, optimal subpopulation, and optimal weighted average treatment effects. Ad hoc methods have been developed for each estimand based on inverse probability weighting (IPW) and on outcome regression modeling, but these may be sensitive to model misspecification, practical violations of positivity, or both. The contribution of this article is twofold. First, we formulate the generalized average treatment effect (GATE) to unify these causal estimands as well as their IPW estimates. Second, we develop a method based on Kernel optimal matching (KOM) to optimally estimate GATE and to find the GATE most easily estimable by KOM, which we term the Kernel optimal weighted average treatment effect. KOM provides uniform control on the conditional mean squared error of a weighted estimator over a class of models while simultaneously controlling for precision. We study its theoretical properties and evaluate its comparative performance in a simulation study. We illustrate the use of KOM for GATE estimation in two case studies: comparing spine surgical interventions and studying the effect of peer support on people living with HIV.

Open Access July 14, 2022

A note on efficient minimum cost adjustment sets in causal graphical models

Ezequiel Smucler, Andrea Rotnitzky

Page range: 174-189

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Abstract

We study the selection of adjustment sets for estimating the interventional mean under an individualized treatment rule. We assume a non-parametric causal graphical model with, possibly, hidden variables and at least one adjustment set composed of observable variables. Moreover, we assume that observable variables have positive costs associated with them. We define the cost of an observable adjustment set as the sum of the costs of the variables that comprise it. We show that in this setting there exist adjustment sets that are minimum cost optimal, in the sense that they yield non-parametric estimators of the interventional mean with the smallest asymptotic variance among those that control for observable adjustment sets that have minimum cost. Our results are based on the construction of a special flow network associated with the original causal graph. We show that a minimum cost optimal adjustment set can be found by computing a maximum flow on the network, and then finding the set of vertices that are reachable from the source by augmenting paths. The optimaladj Python package implements the algorithms introduced in this article.

Open Access July 22, 2022

Estimating marginal treatment effects under unobserved group heterogeneity

Tadao Hoshino, Takahide Yanagi

Page range: 197-216

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Abstract

This article studies the treatment effect models in which individuals are classified into unobserved groups based on heterogeneous treatment rules. By using a finite mixture approach, we propose a marginal treatment effect (MTE) framework in which the treatment choice and outcome equations can be heterogeneous across groups. Under the availability of instrumental variables specific to each group, we show that the MTE for each group can be separately identified. On the basis of our identification result, we propose a two-step semiparametric procedure for estimating the group-wise MTE. We illustrate the usefulness of the proposed method with an application to economic returns to college education.

Open Access September 20, 2022

Properties of restricted randomization with implications for experimental design

Mattias Nordin, Mårten Schultzberg

Page range: 227-245

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Abstract

Recently, there has been increasing interest in the use of heavily restricted randomization designs which enforce balance on observed covariates in randomized controlled trials. However, when restrictions are strict, there is a risk that the treatment effect estimator will have a very high mean squared error (MSE). In this article, we formalize this risk and propose a novel combinatoric-based approach to describe and address this issue. First, we validate our new approach by re-proving some known properties of complete randomization and restricted randomization. Second, we propose a novel diagnostic measure for restricted designs that only use the information embedded in the combinatorics of the design. Third, we show that the variance of the MSE of the difference-in-means estimator in a randomized experiment is a linear function of this diagnostic measure. Finally, we identify situations in which restricted designs can lead to an increased risk of getting a high MSE and discuss how our diagnostic measure can be used to detect such designs. Our results have implications for any restricted randomization design and can be used to evaluate the trade-off between enforcing balance on observed covariates and avoiding too restrictive designs.

Open Access September 22, 2022

Clarifying causal mediation analysis: Effect identification via three assumptions and five potential outcomes

Trang Quynh Nguyen, Ian Schmid, Elizabeth L. Ogburn, Elizabeth A. Stuart

Page range: 246-279

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Abstract

Causal mediation analysis is complicated with multiple effect definitions that require different sets of assumptions for identification. This article provides a systematic explanation of such assumptions. We define five potential outcome types whose means are involved in various effect definitions. We tackle their mean/distribution’s identification, starting with the one that requires the weakest assumptions and gradually building up to the one that requires the strongest assumptions. This presentation shows clearly why an assumption is required for one estimand and not another, and provides a succinct table from which an applied researcher could pick out the assumptions required for identifying the causal effects they target. Using a running example, the article illustrates the assembling and consideration of identifying assumptions for a range of causal contrasts. For several that are commonly encountered in the literature, this exercise clarifies that identification requires weaker assumptions than those often stated in the literature. This attention to the details also draws attention to the differences in the positivity assumption for different estimands, with practical implications. Clarity on the identifying assumptions of these various estimands will help researchers conduct appropriate mediation analyses and interpret the results with appropriate caution given the plausibility of the assumptions.

Review Article

Open Access July 1, 2022

Causal inference in AI education: A primer

Andrew Forney, Scott Mueller

Page range: 141-173

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Abstract

The study of causal inference has seen recent momentum in machine learning and artificial intelligence (AI), particularly in the domains of transfer learning, reinforcement learning, automated diagnostics, and explainability (among others). Yet, despite its increasing application to address many of the boundaries in modern AI, causal topics remain absent in most AI curricula. This work seeks to bridge this gap by providing classroom-ready introductions that integrate into traditional topics in AI, suggests intuitive graphical tools for the application to both new and traditional lessons in probabilistic and causal reasoning, and presents avenues for instructors to impress the merit of climbing the “causal hierarchy” to address problems at the levels of associational, interventional, and counterfactual inference. Finally, this study shares anecdotal instructor experiences, successes, and challenges integrating these lessons at multiple levels of education.

Commentary

Open Access July 15, 2022

Comment on: “Decision-theoretic foundations for statistical causality”

Ilya Shpitser

Page range: 190-196

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Open Access August 16, 2022

Decision-theoretic foundations for statistical causality: Response to Shpitser

Philip Dawid

Page range: 217-220

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Abstract

I thank Ilya Shpitser for his comments on my article, and discuss the use of models with restricted interventions.

Volume 7 (2019)

Volume 6 (2018)

Volume 5 (2017)

Volume 4 (2016)

Volume 3 (2015)

Volume 2 (2014)

Volume 1 (2013)

5-year Impact Factor	4.446
Cite Score	2.9
Impact Factor	1.867
Journal Citation Indicator	0.80
SCImago Journal Rank	0.975
Source Normalized Impact per Paper	1.321

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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 coauthors. Where there are others who have participated in certain substantive aspects of the research project, they should be named in an Acknowledgement section.

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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. The journal uses single-blind peer review model. 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. Suggested reviewers must not include authors’ students, mentors, collaborators, or others who have conflicts of interest. The final decision of acceptance is made by the main Editor of the journal.

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Effective January 2021, the journal requires authors to follow data sharing policy. Research data should be made widely available to the research community in order to demonstrate the robustness and validity of the research presented in the journal, to encourage replication of the results, and to provide the community with opportunities to learn. By publishing in the journal authors are required to provide a data availability statement (DAS) in their articles. Authors are encouraged to share their data but not required to. The decision to publish will not be affected by whether or not authors share their research data.

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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 Code of Conduc t and implement its advice.

Editorial

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, Massachusetts Institute of Technology, USA
Peter Aronow, Yale University, USA
Laura Balzer, University of Massachusetts, Amherst, USA
Elias Bareinboim, Columbia University, 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
Ivan Diaz, Weill Cornell Medicine, USA
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
Jens Hainmueller, Stanford University, USA
Joseph Halpern, Cornell University, 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, University of Copenhagen, Denmark
Sam Pimentel, University of California, Berkeley, USA
Michael Rosenblum, Johns Hopkins University, USA
Andrea Rotnitzky, Universidad Torcuato Di Tella, Argentina
Helene Rytgaard, University of Copenhagen, Denmark
Ilya Shpitser, Johns Hopkins University, USA
Ricardo Silva, University College London, UK
Elizabeth Stuart, Johns Hopkins University, USA
Eric Tchetgen Tchetgen, Harvard University, USA
Jin Tian, Iowa State University, USA
Tyler VanderWeele, Harvard University, USA
Teppei Yamamoto, Massachusetts Institute of Technology, USA

Editorial Contact
Monika Maleszka
jci_editorial@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