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

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

Purchase of the print or Kindle book includes a free PDF eBook

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

Book Description:

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

Who this book is for:

This book is for machine learning engineers, data scientists, and machine learning researchers looking to extend their data science toolkit and explore causal machine learning. It will also help developers familiar with causality who have worked in another technology and want to switch to Python, and data scientists with a history of working with traditional causality who want to learn causal machine learning. It's also a must-read for tech-savvy entrepreneurs looking to build a competitive edge for their products and go beyond the limitations of traditional machine learning.

解密因果推論和因果發現，通過揭示因果原則並將其與強大的機器學習算法結合，用於觀察和實驗數據。

購買印刷版或Kindle電子書，將包含免費的PDF電子書。

主要特點：
- 深入研究Pearlian因果概念，如結構性因果模型、干預、反事實等。
- 探索現代因果推論技術，用於平均和異質治療效果估計。
- 探索並利用傳統和現代因果發現方法。

書籍描述：
因果方法與傳統的機器學習和統計學相比，具有獨特的挑戰。學習因果關係可能具有挑戰性，但它提供了純統計思維所無法捉摸的明顯優勢。《Python中的因果推論和發現》幫助您發掘因果關係的潛力。

您將從對因果思維的基本動機和Pearlian因果概念的全面介紹開始，例如結構性因果模型、干預、反事實等。每個概念都附有理論解釋和一組帶有Python代碼的實踐練習。

接下來，您將深入研究因果效應估計，並持續進展到現代機器學習方法。逐步進展，您將探索Python因果生態系統，並利用尖端算法的威力。您還將探索“原因留下痕跡”的機制，並比較主要的因果發現算法家族。

最後一章將向您展示因果人工智能的未來前景，我們將檢視挑戰和機遇，並為您提供一份全面的學習資源清單。

您將學到什麼：
- 掌握因果推論的基本概念。
- 解讀結構性因果模型的奧秘。
- 在Python中發揮4步因果推論過程的威力。
- 探索高級提升建模技術。
- 解鎖使用Python進行現代因果發現的秘密。
- 將因果推論應用於社會影響和社區利益。

本書適合對機器學習工程師、數據科學家和機器學習研究人員，希望擴展其數據科學工具包並探索因果機器學習的人士。它還將幫助熟悉因果關係的開發人員，他們在其他技術領域工作並希望轉向Python，以及具有傳統因果關係工作經驗並希望學習因果機器學習的數據科學家。對於希望為其產品建立競爭優勢並超越傳統機器學習的技術精通企業家來說，這也是一本必讀之書。