Drawing and Analyzing Causal DAGs with DAGitty
It provides a practical tool for researchers and students in empirical disciplines to improve causal inference, though it is incremental as it builds on existing DAG methodology.
DAGitty is a software tool for drawing and analyzing causal diagrams (DAGs) to help researchers identify adjustment sets, diagnose biases, and derive testable implications in fields like epidemiology and sociology.
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.