|
- DAGitty - drawing and analyzing causal diagrams (DAGs)
DAGitty is a browser-based environment for creating, editing, and analyzing causal diagrams (also known as directed acyclic graphs or causal Bayesian networks)
- DAGitty v3. 1
ancestor of exposure ancestor of outcome ancestor of exposure and outcome
- Drawing and Analyzing Causal DAGs with DAGitty
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 that supports JavaScript, HTML, and SVG This is the user manual for DAGitty version 3 1
- Learn more about DAGs and DAGitty
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 diagrams
- Causal Diagrams Cheat Sheet - DAGitty
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 know what it’s for, and also lets dagitty do some nice calculations for you
- Terminology in Causal Diagrams: Ancestral Relations - DAGitty
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) consist of nodes that represent variables (e g X, Y) and arrows that depict direct causal effects A very simple DAG is the following:
- Terminology in Causal Diagrams: Covariate Roles - DAGitty
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 distinguish two variables of interest: the exposure, or independent variable, or cause, and the outcome, or dependent variable, or effect Once these two special variables are selected, the other
- Causal Intepretation of Multiple Regression: The Table 2 Fallacy - DAGitty
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 variety of different roles from a causal inference perspective: they can be mediators, confounders, proxy confounders, or competing exposures If a suitable set of covariates can be identified
|
|
|