Free Download Udemy – Causal AI in a Nutshell

Published: 12/2024
Created by: CausAI B.V.
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All | Genre: eLearning | Language: English | Duration: 22 Lectures ( 1h 16m ) | Size: 435 MB

Free Download Steven Sloman, "Causal Models: How People Think about the World and Its Alternatives"
English | 2005 | pages: 218 | ISBN: 0195183118, 0195394291 | PDF | 2,2 mb
Human beings are active agents who can think. To understand how thought serves action requires understanding how people conceive of the relation between cause and effect, between action and outcome. In cognitive terms, how do people construct and reason with the causal models we use to represent our world? A revolution is occurring in how statisticians, philosophers, and computer scientists answer this question. Those fields have ushered in new insights about causal models by thinking about how to represent causal structure mathematically, in a framework that uses graphs and probability theory to develop what are called causal Bayesian networks. The framework starts with the idea that the purpose of causal structure is to understand and predict the effects of intervention. How does intervening on one thing affect other things? This is not a question merely about probability (or logic), but about action. The framework offers a new understanding of mind: Thought is about the effects of intervention and cognition is thus intimately tied to actions that take place either in the actual physical world or in imagination, in counterfactual worlds. The book offers a conceptual introduction to the key mathematical ideas, presenting them in a non-technical way, focusing on the intuitions rather than the theorems. It tries to show why the ideas are important to understanding how people explain things and why thinking not only about the world as it is but the world as it could be is so central to human action. The book reviews the role of causality, causal models, and intervention in the basic human cognitive functions: decision making, reasoning, judgment, categorization, inductive inference, language, and learning. In short, the book offers a discussion about how people think, talk, learn, and explain things in causal terms, in terms of action and manipulation.

Free Download The Routledge Handbook of Causality and Causal Methods
by Phyllis Illari, Federica Russo
English | 2025 | ISBN: 103226019X | 717 Pages | True ePUB | 3.72 MB

Free Download The Routledge Handbook of Causality and Causal Methods
by Phyllis Illari, Federica Russo
English | 2025 | ISBN: 103226019X | 717 Pages | True PDF | 19 MB

Free Download A First Course in Causal Inference
by Peng Ding
English | 2024 | ISBN: 1032758627 | 448 pages | True PDF | 28.45 MB

Free Download Artificial Intelligence and Causal Inference (Chapman & Hall/CRC Machine Learning & Pattern Recognition) by Momiao Xiong
English | March 8, 2022 | ISBN: 0367859408 | 394 pages | MOBI | 4.83 Mb
Artificial Intelligence and Causal Inference address the recent development of relationships between artificial intelligence (AI) and causal inference. Despite significant progress in AI, a great challenge in AI development we are still facing is to understand mechanism underlying intelligence, including reasoning, planning and imagination. Understanding, transfer and generalization are major principles that give rise intelligence. One of a key component for understanding is causal inference. Causal inference includes intervention, domain shift learning, temporal structure and counterfactual thinking as major concepts to understand causation and reasoning. Unfortunately, these essential components of the causality are often overlooked by machine learning, which leads to some failure of the deep learning. AI and causal inference involve (1) using AI techniques as major tools for causal analysis and (2) applying the causal concepts and causal analysis methods to solving AI problems. The purpose of this book is to fill the gap between the AI and modern causal analysis for further facilitating the AI revolution. This book is ideal for graduate students and researchers in AI, data science, causal inference, statistics, genomics, bioinformatics and precision medicine.

Free Download Causal Inference in R:
Decipher complex relationships with advanced R techniques for data-driven decision-making
English | 2024 | ISBN: 1837639027 | 665 Pages | EPUB (True) | 10 MB

Free Download Causal Inference and Discovery in Python: Unlock the secrets of modern causal machine learning with DoWhy, EconML, PyTorch and more by Aleksander Molak
English | May 31, 2023 | ISBN: 1804612987 | 456 pages | PDF | 11 Mb
Book Description

Free Download Causal AI (MEAP 04)
English | 2023 | ISBN: 9781633439917 | 219 pages | MOBI | 2.44 Mb
How do you know what might have happened, had you done things differently? Causal machine learning gives you the insight you need to make predictions and control outcomes based on causal relationships instead of pure correlation, so you can make precise and timely interventions.

Free Download A Primer to Causal Reasoning About a Complex World by Lars-Göran Johansson , Thomas Banitz , Volker Grimm , Tilman Hertz , Emilie Lindkvist , Rodrigo Martínez Peña , Sonja Radosavljevic , Petri Ylikoski , Maja Schlüter
English | PDF EPUB (True) | 2024 | 149 Pages | ISBN : 3031591348 | 7.3 MB
This book is about causal thinking and the use of causal language, with a focus on introducing philosophical ideas about causation to students and researchers of Social-Ecological Systems (SES). It takes a systematic approach to three central topics: the meanings of different causal expressions, sufficiency of evidence for inferences from observations to causal relations, and how to handle the complexity of causal relations in social-ecological systems. Consequently, the book is divided into three parts. In the first part the authors analyse and discuss the use of causal idiom in ordinary language, and in the second part they scrutinise the use of causal concepts and causal inference in science. Finally, the authors discuss causal reasoning about social-ecological systems in multi- and interdisciplinary contexts.