Tag: Causal

Free Download Causal Artificial Intelligence
by Hurwitz, Judith S.;Thompson, John K.;
English | 2023 | ISBN: 1394184131 | 387 pages | True PDF | 8.01 MB
Discover the next major revolution in data science and AI and how it applies to your organization In Causal Artificial The Next Step in Effective, Efficient, and Practical AI , a team of dedicated tech executives delivers a business-focused approach based on a deep and engaging exploration of the models and data used in causal AI. The book’s discussions include both accessible and understandable technical detail and business context and concepts that frame causal AI in familiar business settings. Useful for both data scientists and business-side professionals, the book An enlightening and easy-to-understand treatment of an essential business topic, Causal Artificial Intelligence is a must-read for data scientists, subject matter experts, and business leaders seeking to familiarize themselves with a rapidly growing area of AI application and research.

Free Download Causal AI – A Tech Primer
Released 7/2023
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch
Skill Level: Intermediate | Genre: eLearning | Language: English + srt | Duration: 25m | Size: 50m MB
Are you interested in developing your understanding of causal AI and how it’s used? In this course, designed specifically for AI and data science professionals, explore the advantages and disadvantages of causal AI with instructor Robert Joseph.

Free Download Causal Inference in Python: Applying Causal Inference in the Tech Industry by Matheus Facure
English | August 22nd, 2023 | ISBN: 1098140257 | 406 pages | True EPUB (Retail Copy) | 9.46 MB
How many buyers will an additional dollar of online marketing bring in? Which customers will only buy when given a discount coupon? How do you establish an optimal pricing strategy? The best way to determine how the levers at our disposal affect the business metrics we want to drive is through causal inference.

Free Download Causal Inference in Python: Applying Causal Inference in the Tech Industry
English | 2023 | ISBN: 1098140257 | 496 Pages | EPUB (True) | 9.2 MB
How many buyers will an additional dollar of online marketing bring in? Which customers will only buy when given a discount coupon? How do you establish an optimal pricing strategy? The best way to determine how the levers at our disposal affect the business metrics we want to drive is through causal inference.

Free Download Bayesian Nonparametrics for Causal Inference and Missing Data
English | 2023 | ISBN: 9780429324222 | 262 pages | PDF | 5.2 MB
Bayesian Nonparametrics for Causal Inference and Missing Data provides an overview of flexible Bayesian nonparametric (BNP) methods for modeling joint or conditional distributions and functional relationships, and their interplay with causal inference and missing data. This book emphasizes the importance of making untestable assumptions to identify estimands of interest, such as missing at random assumption for missing data and unconfoundedness for causal inference in observational studies. Unlike parametric methods, the BNP approach can account for possible violations of assumptions and minimize concerns about model misspecification. The overall strategy is to first specify BNP models for observed data and then to specify additional uncheckable assumptions to identify estimands of interest.

Free Download Hector Geffner, Rita Dechter, Joseph Y. Halpern, "Probabilistic and Causal Inference: The Works of Judea Pearl"
English | 2022 | ISBN: 1450395864, 1450395872 | PDF | pages: 946 | 20.0 mb
Professor Judea Pearl won the 2011 Turing Award "for fundamental contributions to artificial intelligence through the development of a calculus for probabilistic and causal reasoning." This book contains the original articles that led to the award, as well as other seminal works, divided into four parts: heuristic search, probabilistic reasoning, causality, first period (1988-2001), and causality, recent period (2002-2020). Each of these parts starts with an introduction written by Judea Pearl. The volume also contains original, contributed articles by leading researchers that analyze, extend, or assess the influence of Pearl’s work in different fields: from AI, Machine Learning, and Statistics to Cognitive Science, Philosophy, and the Social Sciences. The first part of the volume includes a biography, a transcript of his Turing Award Lecture, two interviews, and a selected bibliography annotated by him.

Free Download Causal AI (MEAP 04)
English | 2023 | ISBN: 9781633439917 | 219 Pages | PDF EPUB | 8 MB
Causal AI is a practical introduction to building AI models that can reason about causality. Author Robert Ness, a leading researcher in causal AI at Microsoft Research, brings his unique expertise to this cutting-edge guide. His clear, code-first approach explains essential details of causal machine learning that are hidden in academic papers. Everything you learn can be easily and effectively applied to industry challenges, from building explainable causal models to predicting counterfactual outcomes.

Free Download Causal Inference and Discovery in Python:
Unlock the secrets of modern causal machine learning with DoWhy, EconML, PyTorch and more
English | 2023 | ISBN: 1804612987 | 456 Pages | EPUB (True) | 10 MB

Free Download Implementing Multifactor Authentication
by Marco Fanti
English | 2023 | ISBN: 1804612987 | 456 pages | True/Retail PDF EPUB | 21.11 MB

Free Download Experimental and Quasi-Experimental Designs for Generalized Causal Inference By William R. Shadish, Thomas D. Cook, Donald T. Campbell
2002 | 646 Pages | ISBN: 0395615569 | PDF | 42 MB
This is a book for those who have already decided that identifying a dependable relationship between a cause and its effects is a high priority and who wish to consider experimental methods for doing so. Such causal relationships are of great importance in human affairs. The rewards associated with being correct in identifying causal relationships can be high, an the costs of misidentification can be tremendous. This book has two major purposes: to describe ways in which testing causal propositions can be improved in specific research projects, and to describe ways to improve generalizations about causal propositions. This long awaited successor of the original Cook/Campbell Quasi-Experimentation: Design and Analysis Issues for Field Settings represents updates in the field over the last two decades. The book covers four major topics in field experimentation:- Theoretical matters: Experimentation, causation, and validity- Quasi-experimental design: Regression discontinuity designs, interrupted time series designs, quasi-experimental designs that use both pretests and control groups, and other designs- Randomized experiments: Logic and design issues, and practical problems involving ethics, recruitment, assignment, treatment implementation, and attrition- Generalized causal inference: A grounded theory of generalized causal inference, along with methods for implementing that theory in single and multiple studies