[ PDF ] Ebook Causal Inference and Discovery in Python Unlock the secrets of modern causal machine learning with DoWhy EconML PyTorch and more Online Book

Causal Inference and Discovery in Python:
Unlock the secrets of modern causal
machine learning with DoWhy, EconML,
PyTorch and more
[ PDF ] Ebook Causal Inference and Discovery in Python: Unlock the secrets of
modern causal machine learning with DoWhy, EconML, PyTorch and more Online
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[ PDF ] Ebook Causal Inference and Discovery in Python: Unlock the secrets of
modern causal machine learning with DoWhy, EconML, PyTorch and more Online
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Description
Demystify causal inference and casual discovery by uncovering causal principles and merging
them with powerful machine learning algorithms for observational and experimental dataPurchase
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.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, 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.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