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

Causality and time series – when an econometrician goes Bayesian

In this section, we’re going to introduce a new style of thinking about causality.

We’ll start this section with a brief overview of quasi-experimental methods. Next, we’ll take a closer look at one of these methods – the synthetic control estimator. We’ll implement the synthetic control estimator using an open source package, CausalPy, from PyMC Labs and test it on real-life data.

Quasi-experiments

Randomized controlled trials (RCTs) are often considered the “gold standard” for causal inference. One of the challenges regarding RCTs is that we cannot carry them out in certain scenarios.

On the other hand, there’s a broad class of circumstances where we can observe naturally occurring interventions that we cannot control or randomize. Something naturally changes in the world, and we are interested in understanding the impact of such an event on...