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Alex Ruiz de Villa
Causal Inference for Data Science

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 pr…

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Causal Inference and American Political Development

This volume discusses the application of causal inference techniques in the study of American political development (APD). Within political science, a movement focused on increasing the credibility of cau…

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Sharon Schwartz, Seth J Prins
Causal Inference and the People's Health

In Causality and the People's Health, Sharon Schwartz and Seth J. Prins offer both a synthesis of the dominant school of thought around social causality and propose a new approach that keeps causal concep…

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Aboozar Hadavand
Cause, Effect, and Everything in Between

Cause, Effect, and Everything in Between introduces readers to causal inference: the science of cause-and-effect. Using examples and case studies, Aboozar Hadavand provides an accessible introduction to t…

lieferbar ab 21.8.2025.

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Aboozar Hadavand
Cause, Effect, and Everything in Between

Cause, Effect, and Everything in Between introduces readers to causal inference: the science of cause-and-effect. Using examples and case studies, Aboozar Hadavand provides an accessible introduction to t…

lieferbar ab 21.8.2025.

Buch

Recent Advancements in Tourism Business, Technology and Social Sciences

The book features the second volume of the proceedings of the 10th International Conference of the International Association of Cultural and Digital Tourism (IACuDiT), with the theme Recent Advancements …

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Recent Advancements in Tourism Business, Technology and Social Sciences

The book features the first volume of the proceedings of the 10th International Conference of the International Association of Cultural and Digital Tourism (IACuDiT), with the theme Recent Advancements i…

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Juan Guillermo Diaz Ochoa
Complexity Measurements and Causation for Dynamic Complex Systems

This book examines the problems of causal determinism and limited completeness in systems theory. Furthermore, the author analyzes options for complexity measurements that include systems' autonomy and va…

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Robert Osazuwa Ness
Causal AI

Build AI models that can reliably deliver causal inference. How do you know what might have happened, had you done things differently? Causal AI gives you the insight you need to make predictions and con…

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Quantitative Psychology

The volume represents presentations given at the 87th annual meeting of the Psychometric Society, held in Bologna, Italy at July 11 15, 2022. The proceedings cover a diverse set of psychometric topics, in…

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Saniya
Beyond Mediation and Modification: A Deeper Look at Cause and Effect

The rest of this book is organized as follows. Chapter 2 covers the literature review of the Neyman-Rubin s counterfactual framework of causal inference, general assumptions for causal studies, Pearl s gr…

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Armando Barrientos
Social Protection in Latin America

This book offers a comprehensive analysis of social protection in Latin America, its origins, institutions, and outcomes. The chapters are organised in three groups. The earlier chapters discuss in turn a…

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Joseph Simonian
Computational Global Macro

Computational Global Macro offers investors a new paradigm for the analysis of geopolitical risk. By drawing on game theory, machine learning, and causal inference, the book provides investors with a nove…

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Deep Generative Models

Lecture Notes in Computer Science - Band 15224

This book constitutes the proceedings of the 4th workshop on Deep Generative Models for Medical Image Computing and Computer Assisted Intervention, DGM4MICCAI 2024, held in conjunction with the 27th Inter…

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Judea Pearl
The Book of Why

Judea Pearl (Author) Judea Pearl is a world-renowned Israeli-American computer scientist and philosopher, known for his world-leading work in AI and the development of Bayesian networks, as well as his t…

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Qing Zhou
Latent Structure And Causality

Inferring latent structure and causality is crucial for understanding underlying patterns and relationships hidden in the data. This book covers selected models for latent structures and causal networks a…

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