Machine Learning For Causal Inference
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Machine Learning For Causal Inference
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Author : Sheng Li
language : en
Publisher: Springer Nature
Release Date : 2023-11-25
Machine Learning For Causal Inference written by Sheng Li and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-11-25 with Computers categories.
This book provides a deep understanding of the relationship between machine learning and causal inference. It covers a broad range of topics, starting with the preliminary foundations of causal inference, which include basic definitions, illustrative examples, and assumptions. It then delves into the different types of classical causal inference methods, such as matching, weighting, tree-based models, and more. Additionally, the book explores how machine learning can be used for causal effect estimation based on representation learning and graph learning. The contribution of causal inference in creating trustworthy machine learning systems to accomplish diversity, non-discrimination and fairness, transparency and explainability, generalization and robustness, and more is also discussed. The book also provides practical applications of causal inference in various domains such as natural language processing, recommender systems, computer vision, time series forecasting, and continual learning. Each chapter of the book is written by leading researchers in their respective fields. Machine Learning for Causal Inference explores the challenges associated with the relationship between machine learning and causal inference, such as biased estimates of causal effects, untrustworthy models, and complicated applications in other artificial intelligence domains. However, it also presents potential solutions to these issues. The book is a valuable resource for researchers, teachers, practitioners, and students interested in these fields. It provides insights into how combining machine learning and causal inference can improve the system's capability to accomplish causal artificial intelligence based on data. The book showcases promising research directions and emphasizes the importance of understanding the causal relationship to construct different machine-learning models from data.
Elements Of Causal Inference
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Author : Jonas Peters
language : en
Publisher:
Release Date : 2017
Elements Of Causal Inference written by Jonas Peters and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017 with Causation categories.
Targeted Learning In Data Science
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Author : Mark J. van der Laan
language : en
Publisher: Springer
Release Date : 2018-03-28
Targeted Learning In Data Science written by Mark J. van der Laan and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-03-28 with Mathematics categories.
This textbook for graduate students in statistics, data science, and public health deals with the practical challenges that come with big, complex, and dynamic data. It presents a scientific roadmap to translate real-world data science applications into formal statistical estimation problems by using the general template of targeted maximum likelihood estimators. These targeted machine learning algorithms estimate quantities of interest while still providing valid inference. Targeted learning methods within data science area critical component for solving scientific problems in the modern age. The techniques can answer complex questions including optimal rules for assigning treatment based on longitudinal data with time-dependent confounding, as well as other estimands in dependent data structures, such as networks. Included in Targeted Learning in Data Science are demonstrations with soft ware packages and real data sets that present a case that targeted learning is crucial for the next generation of statisticians and data scientists. Th is book is a sequel to the first textbook on machine learning for causal inference, Targeted Learning, published in 2011. Mark van der Laan, PhD, is Jiann-Ping Hsu/Karl E. Peace Professor of Biostatistics and Statistics at UC Berkeley. His research interests include statistical methods in genomics, survival analysis, censored data, machine learning, semiparametric models, causal inference, and targeted learning. Dr. van der Laan received the 2004 Mortimer Spiegelman Award, the 2005 Van Dantzig Award, the 2005 COPSS Snedecor Award, the 2005 COPSS Presidential Award, and has graduated over 40 PhD students in biostatistics and statistics. Sherri Rose, PhD, is Associate Professor of Health Care Policy (Biostatistics) at Harvard Medical School. Her work is centered on developing and integrating innovative statistical approaches to advance human health. Dr. Rose’s methodological research focuses on nonparametric machine learning for causal inference and prediction. She co-leads the Health Policy Data Science Lab and currently serves as an associate editor for the Journal of the American Statistical Association and Biostatistics.
Causal Inference For Data Science
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Author : Aleix Ruiz de Villa Robert
language : en
Publisher: Simon and Schuster
Release Date : 2025-02-18
Causal Inference For Data Science written by Aleix Ruiz de Villa Robert and has been published by Simon and Schuster this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-02-18 with Computers categories.
When you know the cause of an event, you can affect its outcome. This accessible introduction to causal inference shows you how to determine causality and estimate effects using statistics and machine learning. A/B tests or randomized controlled trials are expensive and often unfeasible in a business environment. Causal Inference for Data Science reveals the techniques and methodologies you can use to identify causes from data, even when no experiment or test has been performed. In Causal Inference for Data Science you will learn how to: • Model reality using causal graphs • Estimate causal effects using statistical and machine learning techniques • Determine when to use A/B tests, causal inference, and machine learning • Explain and assess objectives, assumptions, risks, and limitations • Determine if you have enough variables for your analysis It’s possible to predict events without knowing what causes them. Understanding causality allows you both to make data-driven predictions and also intervene to affect the outcomes. Causal Inference for Data Science shows you how to build data science tools that can identify the root cause of trends and events. You’ll learn how to interpret historical data, understand customer behaviors, and empower management to apply optimal decisions. About the technology Why did you get a particular result? What would have lead to a different outcome? These are the essential questions of causal inference. This powerful methodology improves your decisions by connecting cause and effect—even when you can’t run experiments, A/B tests, or expensive controlled trials. About the book Causal Inference for Data Science introduces techniques to apply causal reasoning to ordinary business scenarios. And with this clearly-written, practical guide, you won’t need advanced statistics or high-level math to put causal inference into practice! By applying a simple approach based on Directed Acyclic Graphs (DAGs), you’ll learn to assess advertising performance, pick productive health treatments, deliver effective product pricing, and more. What's inside • When to use A/B tests, causal inference, and ML • Assess objectives, assumptions, risks, and limitations • Apply causal inference to real business data About the reader For data scientists, ML engineers, and statisticians. About the author Aleix Ruiz de Villa Robert is a data scientist with a PhD in mathematical analysis from the Universitat Autònoma de Barcelona. Table of Contents Part 1 1 Introducing causality 2 First steps: Working with confounders 3 Applying causal inference 4 How machine learning and causal inference can help each other Part 2 5 Finding comparable cases with propensity scores 6 Direct and indirect effects with linear models 7 Dealing with complex graphs 8 Advanced tools with the DoubleML library Part 3 9 Instrumental variables 10 Potential outcomes framework 11 The effect of a time-related event A The math behind the adjustment formula B Solutions to exercises in chapter 2 C Technical lemma for the propensity scores D Proof for doubly robust estimator E Technical lemma for the alternative instrumental variable estimator F Proof of the instrumental variable formula for imperfect compliance
Causal Inference For Data Science
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Author : Alex Ruiz de Villa
language : en
Publisher: Simon and Schuster
Release Date : 2025-01-21
Causal Inference For Data Science written by Alex Ruiz de Villa and has been published by Simon and Schuster this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-01-21 with Computers categories.
Causal Inference for Data Science introduces data-centric techniques and methodologies you can use to estimate causal effects. The numerous insightful examples show you how to put causal inference into practice in the real world. The practical techniques presented in this unique book are accessible to anyone with intermediate data science skills and require no advanced statistics!
Essays On Using Machine Learning For Causal Inference In Social Science
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Author : Jonathan Bernhard Fuhr
language : en
Publisher:
Release Date : 2024
Essays On Using Machine Learning For Causal Inference In Social Science written by Jonathan Bernhard Fuhr and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024 with categories.
Machine Learning
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Author : Dr. Gowthami S
language : en
Publisher: RK Publication
Release Date : 2025-06-17
Machine Learning written by Dr. Gowthami S and has been published by RK Publication this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-06-17 with Computers categories.
This book offers a comprehensive introduction to Machine Learning, covering fundamental concepts, algorithms, and practical applications. Designed for students, researchers, and professionals, it explores supervised, unsupervised, and reinforcement learning with real-world use cases. Emphasis is placed on model evaluation, optimization, and ethical AI practices in modern data-driven environments.
Machine Learning And Causality The Impact Of Financial Crises On Growth
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Author : Mr.Andrew J Tiffin
language : en
Publisher: International Monetary Fund
Release Date : 2019-11-01
Machine Learning And Causality The Impact Of Financial Crises On Growth written by Mr.Andrew J Tiffin and has been published by International Monetary Fund this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-11-01 with Computers categories.
Machine learning tools are well known for their success in prediction. But prediction is not causation, and causal discovery is at the core of most questions concerning economic policy. Recently, however, the literature has focused more on issues of causality. This paper gently introduces some leading work in this area, using a concrete example—assessing the impact of a hypothetical banking crisis on a country’s growth. By enabling consideration of a rich set of potential nonlinearities, and by allowing individually-tailored policy assessments, machine learning can provide an invaluable complement to the skill set of economists within the Fund and beyond.
50 Breakthrough Machine Learning Techniques In 7 Minutes Each
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Author : Nietsnie Trebla
language : en
Publisher: Shelf Indulgence
Release Date :
50 Breakthrough Machine Learning Techniques In 7 Minutes Each written by Nietsnie Trebla and has been published by Shelf Indulgence this book supported file pdf, txt, epub, kindle and other format this book has been release on with categories.
50 Breakthrough Machine Learning Techniques in 7 Minutes Each Unlock the secrets of machine learning with '50 Breakthrough Machine Learning Techniques in 7 Minutes Each', a concise and engaging guide designed for both beginners and seasoned practitioners. Dive into the revolutionary world of AI as you explore transformative concepts, tools, and methodologies that are reshaping technology and society. Each chapter is crafted to deliver essential knowledge—packed with clarity and depth—allowing you to grasp intricate techniques in mere minutes. Here are some of the captivating chapters you’ll discover: - The Rise of Deep Learning: Explore the foundations and advancements that sparked the AI revolution. - Transformers: Revolutionizing NLP: Learn how transformers have set new benchmarks in natural language processing. - Generative Adversarial Networks (GANs): Understand the mechanics behind this groundbreaking approach to data generation. - Reinforcement Learning in Gaming: Find out how AI is transforming gaming experiences through intelligent behavior. - AutoML: Automating the Machine Learning Pipeline: Discover how automation is simplifying the ML workflow. - Neural Architecture Search: Delve into techniques that optimize model design through smart search algorithms. - Federated Learning: Privacy-Preserving AI: Examine how distributed learning models maintain data privacy while training algorithms. - Explainable AI (XAI): Learn about the importance of transparency in AI decision-making. - Few-Shot and Zero-Shot Learning: Understand approaches that enable models to learn with minimal data. - Transfer Learning for Better Performance: Explore the power of leveraging existing knowledge across tasks. - Graph Neural Networks: Get acquainted with this innovative technique for processing graph-structured data. - Quantum Machine Learning: Discover the potential of quantum computing in advancing machine learning. - Neuro-Symbolic AI: Investigate the integration of neural networks with symbolic reasoning. - Self-Supervised Learning: Learn about learning without labeled data and its growing significance. - Contrastive Learning: Understand this emerging framework for representation learning. - Meta-Learning: Learning to Learn: Delve into techniques that enable algorithms to adapt quickly. - Hyperparameter Optimization: Master the art of fine-tuning models for peak performance. - Data Augmentation Techniques: Enhance your datasets to improve model robustness. - Sequence-to-Sequence Models: Explore architectures suited for sequence prediction tasks. - Attention Mechanisms: Uncover the secret behind focused learning processes in neural networks. - Multi-Modal Learning: Investigate how combining multiple data types can improve results. - Ethics in Machine Learning: Engage with the critical conversations around responsible AI. - Robustness and Adversarial Attack Defense: Learn how to build resilient machine learning systems. - Computer Vision Advances with CNNs: Discover the state-of-the-art techniques in image processing. - Time Series Forecasting with LSTM: Master the application of LSTM networks for sequential data. - Federated Transfer Learning: Explore models that generalize across distributed datasets. - Embedding Techniques: Word2Vec and Beyond: Understand how to represent words in vector space. - Machine Learning for Drug Discovery: Learn how AI is revolutionizing the pharmaceutical industry. - AI in Financial Predictive Analytics: Discover applications of machine learning in finance. - Natural Language Processing with BERT: Grasp the impact of BERT on modern NLP tasks. - Sparse Learning Approaches: Delve into techniques that reduce model complexity while maintaining performance. - Incremental Learning Approaches: Understand how models can learn over time with new data. - AI for Climate Modeling: Explore how machine learning contributes to environmental science. - Evolved Neural Networks: Investigate the future of architecture design through evolutionary principles. - Ensemble Learning Techniques: Learn about combining multiple models for improved accuracy. - Interactive AI: Human-in-the-Loop Systems: Discover how human feedback enhances AI performance. - Causal Inference with Machine Learning: Understand the techniques used to identify causal relationships. - Robotic Process Automation for Social Good: Explore how AI can streamline processes that benefit society. - Recommender Systems Evolution: Learn about the advancements that personalize user experiences. - Blockchain and Machine Learning Synergy: Investigate the intersection of these two groundbreaking technologies. - Edge AI for Real-Time Decision Making: Discover how AI is deployed closer to data sources for instant analysis. - Energy-Efficient Machine Learning: Engage with techniques that reduce the carbon footprint of AI. - Augmented Reality and ML Integration: Understand how machine learning enhances AR experiences. - Voice and Speech Recognition Advances: Explore the latest breakthroughs in human-computer interaction. - ML in Cybersecurity: Learn about the critical role of AI in defending against cyber threats. - Flight Data Analysis with AI: Discover how machine learning optimizes aviation safety and efficiency. - Healthcare Diagnostics through ML: Understand how AI is transforming medical diagnostics and decision-making. - AI-Driven Creative Applications: Explore the intersection of art and AI in the creative process. Whether you’re a student, a professional, or simply curious about machine learning, this book provides a digestible approach to mastering key techniques that will shape the future of technology. Join the revolution and elevate your understanding of AI in just seven minutes at a time!
Machine Learning And Causality The Impact Of Financial Crises On Growth
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Author : Mr.Andrew J Tiffin
language : en
Publisher: International Monetary Fund
Release Date : 2019-11-01
Machine Learning And Causality The Impact Of Financial Crises On Growth written by Mr.Andrew J Tiffin and has been published by International Monetary Fund this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-11-01 with Computers categories.
Machine learning tools are well known for their success in prediction. But prediction is not causation, and causal discovery is at the core of most questions concerning economic policy. Recently, however, the literature has focused more on issues of causality. This paper gently introduces some leading work in this area, using a concrete example—assessing the impact of a hypothetical banking crisis on a country’s growth. By enabling consideration of a rich set of potential nonlinearities, and by allowing individually-tailored policy assessments, machine learning can provide an invaluable complement to the skill set of economists within the Fund and beyond.