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 structure learning

The last source of causal knowledge that we will discuss in this chapter is causal structure learning. Causal structure learning (sometimes used interchangeably with causal discovery) is a set of methods aiming at recovering the structure of the data-generating process from the data generated by this process. Traditional causal discovery focused on recovering the causal structure from observational data only.

Some more recent methods allow for encoding expert knowledge into the graph or learning from interventional data (with known or unknown interventions).

Causal structure learning might be much cheaper and faster than running an experiment, but it often turns out to be challenging in practice.

Many causal structure learning methods require no hidden confounding – a condition difficult to guarantee in numerous real-world scenarios. Some causal discovery methods try to overcome this limitation with some success.

Another challenge is scalability...