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 introduced several methods and ideas that aim to overcome the limitations of traditional causal discovery frameworks. We discussed DECI, an advanced deep learning causal discovery framework, and demonstrated how it can be implemented using Causica, Microsoft’s open source library, and PyTorch.

We explored the FCI algorithm, which can be used to handle data with hidden confounding, and introduced other algorithms that can be used in similar scenarios. These methods provide a strong foundation for tackling complex causal inference problems.

After that, we discussed two frameworks, ENCO and ABCI, that allow us to combine observational and interventional data. These frameworks extend our ability to perform causal discovery and provide valuable tools for data analysis.

Finally, we discussed a number of challenges that we face when applying causal discovery methods to real-world problems.

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