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 discovery – real-world applications, challenges, and open problems

Before we wrap up this chapter, let’s take a broader perspective and discuss the applicability of causal discovery to real-world problems and challenges that may arise along the way.

In the previous chapter, we mentioned that Alexander Reisach and colleagues have demonstrated that the synthetic data used to evaluate causal discovery methods might contain unintended regularities that can be relatively easily exploited by these models (Reisach et al., 2021). The problem is that these regularities might not be present in real-world data.

Another challenge is that real-world data with a known causal structure is scarce. This makes synthetic datasets a natural benchmarking choice, yet this choice leaves us without a clear understanding of what to expect of causal structure learning algorithms when applied to real-world datasets.

The lack of reliable benchmarks is one of the main challenges in...