Drawing and Analyzing Causal DAGs with DAGitty

TL;DR

DAGitty is a web-based software tool designed for drawing and analyzing causal DAGs, aiding researchers in identifying adjustment sets, instrumental variables, and testable implications for causal inference.

Contribution

This paper introduces DAGitty version 2.3, a comprehensive software tool that facilitates causal diagram analysis for empirical research across multiple disciplines.

Findings

Supports identification of minimal adjustment sets

Diagnoses invalid adjustments and biasing paths

Derives testable implications from DAGs

Abstract

DAGitty is a software for drawing and analyzing causal diagrams, also known as directed acyclic graphs (DAGs). Functions include identification of minimal sufficient adjustment sets for estimating causal effects, diagnosis of insufficient or invalid adjustment via the identification of biasing paths, identification of instrumental variables, and derivation of testable implications. DAGitty is provided in the hope that it is useful for researchers and students in Epidemiology, Sociology, Psychology, and other empirical disciplines. The software should run in any web browser that supports modern JavaScript, HTML, and SVG. This is the user manual for DAGitty version 2.3. The manual is updated with every release of a new stable version. DAGitty is available at dagitty.net.