# DAGitty: a graphical tool for analyzing causal diagrams.

@article{Textor2011DAGittyAG, title={DAGitty: a graphical tool for analyzing causal diagrams.}, author={Johannes Textor and Juliane Hardt and Sven Kn{\"u}ppel}, journal={Epidemiology}, year={2011}, volume={22 5}, pages={ 745 } }

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…

## 873 Citations

### Drawing and Analyzing Causal DAGs with DAGitty

- Computer ScienceArXiv
- 2015

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”

- Computer ScienceCurrent protocols
- 2021

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.

- Computer ScienceEpidemiology
- 2017

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

- Economics
- 2013

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

- Economics
- 2013

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

- BusinessDiagrams
- 2020

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

- Computer Science
- 2015

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

- Computer ScienceCWSN
- 2017

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

- Mathematics
- 2017

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.

### GRAPHICAL TOOLS FOR LINEAR PATH MODELS

- Economics
- 2016

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…

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