Causal Inference and Discovery in Python: Unlock the secrets of modern causal machine learning with DoWhy, EconML, PyTorch and more
Synopsis "Causal Inference and Discovery in Python: Unlock the secrets of modern causal machine learning with DoWhy, EconML, PyTorch and more"
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 eBookKey FeaturesExamine Pearlian causal concepts such as structural causal models, interventions, counterfactuals, and moreDiscover modern causal inference techniques for average and heterogenous treatment effect estimationExplore and leverage traditional and modern causal discovery methodsBook DescriptionCausal 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.What you will learnMaster the fundamental concepts of causal inferenceDecipher the mysteries of structural causal modelsUnleash the power of the 4-step causal inference process in PythonExplore advanced uplift modeling techniquesUnlock the secrets of modern causal discovery using PythonUse causal inference for social impact and community benefitWho this book is forThis book is for machine learning engineers, researchers, and data scientists looking to extend their toolkit and explore causal machine learning. It will also help people who've worked with causality using other programming languages and now want to switch to Python, those who worked with traditional causal inference and want to learn about causal machine learning, and tech-savvy entrepreneurs who want to go beyond the limitations of traditional ML. You are expected to have basic knowledge of Python and Python scientific libraries along with knowledge of basic probability and statistics.Table of ContentsCausality - Hey, We Have Machine Learning, So Why Even Bother?Judea Pearl and the Ladder of CausationRegression, Observations, and InterventionsGraphical ModelsForks, Chains, and ImmoralitiesNodes, Edges, and Statistical (In)dependenceThe Four-Step Process of Causal InferenceCausal Models - Assumptions and ChallengesCausal Inference and Machine Learning - from Matching to Meta- LearnersCausal Inference and Machine Learning - Advanced Estimators, Experiments, Evaluations, and MoreCausal Inference and Machine Learning - Deep Learning, NLP, and BeyondCan I Have a Causal Graph, Please?Causal Discovery and Machine Learning - from Assumptions to ApplicationsCausal Discovery and Machine Learning - Advanced Deep Learning and BeyondEpilogue
