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      Robust causal inference using directed acyclic graphs: the R package 'dagitty'.

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          Abstract

          Directed acyclic graphs (DAGs), which offer systematic representations of causal relationships, have become an established framework for the analysis of causal inference in epidemiology, often being used to determine covariate adjustment sets for minimizing confounding bias. DAGitty is a popular web application for drawing and analysing DAGs. Here we introduce the R package 'dagitty', which provides access to all of the capabilities of the DAGitty web application within the R platform for statistical computing, and also offers several new functions. We describe how the R package 'dagitty' can be used to: evaluate whether a DAG is consistent with the dataset it is intended to represent; enumerate 'statistically equivalent' but causally different DAGs; and identify exposure-outcome adjustment sets that are valid for causally different but statistically equivalent DAGs. This functionality enables epidemiologists to detect causal misspecifications in DAGs and make robust inferences that remain valid for a range of different DAGs. The R package 'dagitty' is available through the comprehensive R archive network (CRAN) at [https://cran.r-project.org/web/packages/dagitty/]. The source code is available on github at [https://github.com/jtextor/dagitty]. The web application 'DAGitty' is free software, licensed under the GNU general public licence (GPL) version 2 and is available at [http://dagitty.net/].

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          Most cited references7

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          An open graph visualization system and its applications to software engineering

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            Air Pollution and Hospital Admissions for Heart Disease in Eight U.S. Counties

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              Limitations of individual causal models, causal graphs, and ignorability assumptions, as illustrated by random confounding and design unfaithfulness.

              We describe how ordinary interpretations of causal models and causal graphs fail to capture important distinctions among ignorable allocation mechanisms for subject selection or allocation. We illustrate these limitations in the case of random confounding and designs that prevent such confounding. In many experimental designs individual treatment allocations are dependent, and explicit population models are needed to show this dependency. In particular, certain designs impose unfaithful covariate-treatment distributions to prevent random confounding, yet ordinary causal graphs cannot discriminate between these unconfounded designs and confounded studies. Causal models for populations are better suited for displaying these phenomena than are individual-level models, because they allow representation of allocation dependencies as well as outcome dependencies across individuals. Nonetheless, even with this extension, ordinary graphical models still fail to capture distinctions between hypothetical superpopulations (sampling distributions) and observed populations (actual distributions), although potential-outcome models can be adapted to show these distinctions and their consequences.
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                Author and article information

                Journal
                Int J Epidemiol
                International journal of epidemiology
                Oxford University Press (OUP)
                1464-3685
                0300-5771
                December 01 2016
                : 45
                : 6
                Affiliations
                [1 ] Department of Tumour Immunology, Radboud University Medical Center, P.O. Box 9101, 6500 HB Nijmegen, The Netherlands.
                [2 ] Institute for Theoretical Computer Science, University of Luebeck, Luebeck, Germany.
                [3 ] Leeds Institute of Cardiovascular and Metabolic Medicine.
                [4 ] Leeds Institute for Data Analytics, University of Leeds, Leeds, UK.
                Article
                dyw341
                10.1093/ije/dyw341
                28089956
                06e3703e-3fbf-481b-b959-800ef5f4604a
                History

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