README.qmd

---
title: "Causal Inference in Statistics - dagitty and pgmpy Solutions"
editor:
  markdown: 
    canonical: true
format:
  gfm:
    toc: true
---

This repository contains solutions to the study questions from the book

Pearl J, Glymour M, Jewell NP. Causal Inference in Statistics: A Primer. Wiley, 2016.

The [book's website](http://bayes.cs.ucla.edu/PRIMER/) contains PDFs for each chapter.

There are two sets of solutions, one using the [dagitty](https://cran.r-project.org/web/packages/dagitty/) R package, the other using the [pgmpy](https://pgmpy.org/) Python package.

-   [dagitty solutions](solutions_dagitty.md)
-   [pgmpy solutions](solutions_pgmpy.ipynb)

dagitty was able to solve more of the study questions than pgmpy. Some of the pgmpy algorithms are only suitable for small graphs, whereas the corresponding dagitty algorithms can handle larger graphs.