DAGitty is a browser-based environment for creating, editing, and analyzing causal diagrams (also known as directed acyclic graphs or causal Bayesian networks).

ancestor of exposure ancestor of outcome ancestor of exposure and outcome

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 modern web browser …

If you are just getting started with DAGitty and the manual seems like a little much, check out the DAGitty primer/cheat sheet. It will get you started in using DAGitty to draw and evaluate causal …

Often, you will be using dagitty to attempt to identify the effect of an exposure variable on an outcome variable. Setting the exposure and outcome variables properly lets anyone looking at your graph …

Covariate Roles in DAGs This is based on lecture notes prepared together with Mark Gilthorpe for his module "Advanced Modelling Strategies". Roles of Covariates in DAGs In empirical studies we often …

d -Separation Without Tears Adapted from the original in Judea Pearl's book "Causality" with his permission. Introduction d- separation is a criterion for deciding, from a given a causal graph, …

Variable Relationships in DAGs This is based on lecture notes prepared together with Mark Gilthorpe for his module "Advanced Modelling Strategies". Basic DAG Terminology Causal path diagrams (DAGs) …

The Table 2 Fallacy This is based on lecture notes prepared together with Mark Gilthorpe for his module "Advanced Modelling Strategies". As you know, the covariates in a statistical analysis can have a …

The DAGitty javascript library can be used independently of the graphical user interface, and allows you to embed graphs into your own HTML pages. These graphs are manipulable using the standard …