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

Wrapping it up

In this chapter, we discussed three broad sources of causal knowledge: scientific insights, personal experiences and domain knowledge, and causal structure learning.

We saw that humans start to work on building world models very early in development; yet not all world models that we build are accurate. Heuristics that we use introduce biases that can skew our models on an individual, organizational, or cultural level.

Scientific experiments are an attempt to structure the process of obtaining knowledge so that we can exclude or minimize unwanted interferences and sources of distortion.

Unfortunately, experiments are not always available and have their own limitations. Causal structure learning methods can be cheaper and faster than running experiments, but they might rely on assumptions difficult to meet in certain scenarios.

Hybrid methods that combine causal structure learning, domain expertise, and efficient experimentation are a new exciting field of...