DAGitty: a graphical tool for analyzing causal diagrams.

  title={DAGitty: a graphical tool for analyzing causal diagrams.},
  author={Johannes Textor and Juliane Hardt and Sven Kn{\"u}ppel},
  volume={22 5},
Johannes Textor, Maciej Liskiewicz Identifying and controlling bias is a key problem in empirical sciences. Causal diagram theory provides graphical criteria for deciding whether and how causal effects can be identified from observed (nonexperimental) data by covariate adjustment. Here we prove equivalences between existing as well as new criteria for adjustment and we provide a new simplified but still equivalent notion of d-separation. These lead to efficient algorithms for two important… 

Drawing and Analyzing Causal DAGs with DAGitty

This is the user manual for DAGitty version 2.3.3, which is provided in the hope that it will be useful for researchers and students in Epidemiology, Sociology, Psychology, and other empirical disciplines.

Testing Graphical Causal Models Using the R Package “dagitty”

Here, it is explained how the R package ‘dagitty’, based on the web tool dagitty.net, can be used to test the statistical implications of the assumptions encoded in a given DAG, in the hope that this will help researchers discover model specification errors, avoid erroneous conclusions, and build better models.

Repair of Partly Misspecified Causal Diagrams.

A novel method is presented that repairs a misspecified causal diagram through the addition of edges using a data-driven approach designed to provide improved statistical efficiency relative to de novo structure learning methods.

13 Graphical Causal Models

This chapter discusses the use of directed acyclic graphs (DAGs) for causal inference in the observational social sciences. It focuses on DAGs’ main uses, discusses central principles, and gives

Graphical Causal Models

This chapter discusses the use of directed acyclic graphs (DAGs) for causal inference in the observational social sciences. It focuses on DAGs’ main uses, discusses central principles, and gives

Visual Causality: Investigating Graph Layouts for Understanding Causal Processes

This paper investigates the performance of graph visualisations for supporting users’ understanding of causal graphs and suggests that node-link layouts, and in particular layouts created by a radial algorithm, are more effective for identifying confounder and collider variables.

NDT Perspectives Graphical presentation of confounding in directed acyclic graphs

Examples will show that DAGs can be preferable to the traditional methods to identify sources of confounding, especially in complex research questions.

Finding the Minimal Sufficient Set in Causal Graph Under Interventions: A Dimension Reduction Method for Data Analysis

A criterion to combine the back-door criterion of atomic intervention with conditional intervention when the treatment variable is unique is proposed and can remarkably decrease the complexity of target data analysis regarding data dimension reduction.

Identifying Causal Effects with the R Package causaleffect

The R package causaleffect is presented, which provides an implementation of Shpitser and Pearl's (2006b) algorithm for identifying joint interventional distributions in causal models, which contain unobserved variables and induce directed acyclic graphs.


This article introduces tools for analyzing path diagrams using graphical methods, which are better known in epidemiology than among researchers in the behavioral sciences, who would be more familiar



DAG program: identifying minimal sufficient adjustment sets.

A MS DOS command-line analysis tool, designed to select minimal sufficient adjustment sets within directed acyclic graphs, and the main approach for identifying closed loops in the graph and finding all backdoor paths is the application of the so-called backtracking algorithm.

Reducing bias through directed acyclic graphs

Using the simple 6-step DAG approach to confounding and selection bias discussed is likely to reduce the degree of bias for the effect estimate in the chosen statistical model.

Causal diagrams for epidemiologic research.

Causal diagrams can provide a starting point for identifying variables that must be measured and controlled to obtain unconfounded effect estimates and provide a method for critical evaluation of traditional epidemiologic criteria for confounding.

dagR: a suite of R functions for directed acyclic graphs.

The overall decrease in response rate when offering an Internet option in mailed surveys is consistent with other recent studies and concur with the recommendation by Dillman et al that availability of multiple response modes is likely to bring about more harm than good.

Causality: Models, Reasoning and Inference

1. Introduction to probabilities, graphs, and causal models 2. A theory of inferred causation 3. Causal diagrams and the identification of causal effects 4. Actions, plans, and direct effects 5.

Maternal pesticide exposure from multiple sources and selected congenital anomalies.

Investigating multiple sources of potential pesticide exposures without more specific information on chemical and level of exposure could not adequately discriminate whether the observed effects are valid, whether biased exposure reporting contributed to the observed elevated risks, or whether nonspecific measurement of exposure was responsible for many of the observed estimated risks not being elevated.