000			04047 a2200229 4500
008			240319b |||||||| |||| 00| 0 eng d
020			_a9781804612989 (PB)
041			_aeng
080			_a681.3 _bMOL
100			_aMolak, Aleksander
245			_aCausal Inference and Discovery in Python: _bUnlock the secrets of modern causal machine learning with DoWhy, EconML, PyTorch and more
260			_bPackt Publishing Limited _c2023 _aBirmingham
300			_axxv, 429p.
505			_aTable 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
520			_aDemystify 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 Book 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 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.
650			_aComputer programs
650			_aMachine learning
650			_aPython (Computer program language)
690			_aComputer Science
700			_aJaokar, Ajit
942			_cBK _01
999			_c59954 _d59954