Book Image

Causal Inference and Discovery in Python

By : Aleksander Molak
4.7 (9)
Book Image

Causal Inference and Discovery in Python

4.7 (9)
By: Aleksander Molak

Overview of this book

Causal methods present unique challenges compared to traditional machine learning and statistics. Learning causality can be challenging, but it offers distinct advantages that elude a purely statistical mindset. Causal Inference and Discovery in Python helps you unlock the potential of causality. You’ll start with basic motivations behind causal thinking and a comprehensive introduction to Pearlian causal concepts, such as structural causal models, interventions, counterfactuals, and more. Each concept is accompanied by a theoretical explanation and a set of practical exercises with Python code. Next, you’ll dive into the world of causal effect estimation, consistently progressing towards modern machine learning methods. Step-by-step, you’ll discover Python causal ecosystem and harness the power of cutting-edge algorithms. You’ll further explore the mechanics of how “causes leave traces” and compare the main families of causal discovery algorithms. The final chapter gives you a broad outlook into the future of causal AI where we examine challenges and opportunities and provide you with a comprehensive list of resources to learn more. By the end of this book, you will be able to build your own models for causal inference and discovery using statistical and machine learning techniques as well as perform basic project assessment.
Table of Contents (21 chapters)
Part 1: Causality – an Introduction
Part 2: Causal Inference
Part 3: Causal Discovery

Causal Forests and more

In this short section, we’ll provide a brief overview of the idea behind Causal Forests. We’ll introduce one of the EconML classes implementing the method. An in-depth discussion on Causal Forests and their extensions is beyond the scope of this book, but we’ll point to resources where you can learn more about forest-based causal estimators.

Causal Forest is a tree-based model that stems from the works of Susan Athey, Julie Tibshirani, and Stefan Wager (Wager & Athey, 2018; Athey et al., 2019). The core difference between regular random forest and Causal Forest is that Causal Forest uses so-called causal trees. Otherwise, the methods are similar and both use resampling, predictor subsetting, and averaging over a number of trees.

Causal trees

What makes causal trees different from regular trees is the split criterion. Causal trees use a criterion based on the estimated treatment effects, using so-called honest splitting, where...