Causal Inference and Discovery in Python: Unlock the secrets of modern causal machine learning with DoWhy, EconML, PyTorch and more

English | 2023 | ISBN: 978-1804612989 | 456 Pages | PDF, EPUB | 21 MB

Demystify causal inference and casual discovery by uncovering causal principles and merging them with powerful machine learning algorithms for observational and experimental data

Key Features

• Examine Pearlian causal concepts such as structural causal models, interventions, counterfactuals, and more
• Discover modern causal inference techniques for average and heterogenous treatment effect estimation
• Explore and leverage traditional and modern causal discovery methods

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.

What you will learn

• Master the fundamental concepts of causal inference
• Decipher the mysteries of structural causal models
• Unleash the power of the 4-step causal inference process in Python
• Explore advanced uplift modeling techniques
• Unlock the secrets of modern causal discovery using Python
• Use causal inference for social impact and community benefit