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

We covered a lot in this chapter. We started by revisiting the S-Learner and T-Learner models and demonstrated how flexible deep learning architectures can help combine the benefits of both models. We implemented TARNet and SNet and learned how to use the PyTorch-based CATENets library.

Next, we delved into the application of causality in NLP. We used a Transformer-based CausalBert model to compute the average treatment effect of a gender avatar on the probability of getting an upvote in a simulated Reddit-like discussion forum.

Finally, we took a glimpse into the world of econometrics and quasi-experimental data and learned how to implement a Bayesian synthetic control estimator using CausalPy.

In the next chapter, we’ll start our adventure with causal discovery.

See you on the other side!