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)
1
Part 1: Causality – an Introduction
7
Part 2: Causal Inference
14
Part 3: Causal Discovery

Heterogeneous treatment effects with experimental data – the uplift odyssey

Modeling treatment effects with experimental data is usually slightly different in spirit from working with observational data. This stems from the fact that experimental data is assumed to be unconfounded by design (assuming our experimental design and implementation were not flawed).

In this section, we’ll walk through a workflow of working with experimental data using EconML. We’ll learn how to use EconML’s basic API and see how to work with discrete treatments that have more than two levels. Finally, we’ll use some causal model evaluation metrics in order to compare the models.

The title of this section talks about heterogeneous treatment effects – we already know what they are, but there’s also a new term: uplift. Uplift modeling and heterogeneous (aka conditional) treatment effect modeling are closely related terms. In marketing and medicine, uplift...