Testing Graphical Causal Models Using the R Package “dagitty”

Causal Inference using Graphical Models with the R Package pcalg

The pcalg package for R (R Development Core Team (2010)) can be used for the following two purposes: Causal structure learning and estimation of causal effects from observational data. In this document, we give a brief overview of the methodology, and demonstrate the package’s functionality in both toy examples and applications.

More Causal Inference with Graphical Models in R Package pcalg

The pcalg package for R (R Development Core Team 2014) can be used for the following two purposes: Causal structure learning and estimation of causal effects from observational and/or interventional data. In this document, we give a brief overview of the methodology, and demonstrate the package’s functionality in both toy examples and applications. This vignette is an updated and extended (FCI,...

DAGitty: a graphical tool for analyzing causal diagrams.

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 ...

GGMselect: R package for estimating Gaussian graphical models

Graphical Causal Models

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 applied examples. DAGs are visual representations of qualitative causal assumptions: They encode researchers’ beliefs about how the world works. Straightforward rules map these causal assumptions onto t...