Causal Inference And Discovery In Python
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Causal Inference And Discovery In Python
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Author : Aleksander Molak
language : en
Publisher:
Release Date : 2023
Causal Inference And Discovery In Python written by Aleksander Molak and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023 with categories.
Causal Inference And Discovery In Python
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Author : Aleksander Molak
language : en
Publisher: Packt Publishing Ltd
Release Date : 2023-05-31
Causal Inference And Discovery In Python written by Aleksander Molak and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-05-31 with Computers categories.
Demystify causal inference and casual discovery by uncovering causal principles and merging them with powerful machine learning algorithms for observational and experimental data Get With Your Book: PDF Copy, AI Assistant, and Next-Gen Reader Free 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. By the end of this book, you will be able to build your own models for causal inference and discovery using statistical and machine learning techniques as well as perform basic project assessment.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, researchers, and data scientists looking to extend their toolkit and explore causal machine learning. It will also help people who’ve worked with causality using other programming languages and now want to switch to Python, those who worked with traditional causal inference and want to learn about causal machine learning, and tech-savvy entrepreneurs who want to go beyond the limitations of traditional ML. You are expected to have basic knowledge of Python and Python scientific libraries along with knowledge of basic probability and statistics.
Machine Learning For Civil And Environmental Engineers
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Author : M. Z. Naser
language : en
Publisher: John Wiley & Sons
Release Date : 2023-07-17
Machine Learning For Civil And Environmental Engineers written by M. Z. Naser and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-07-17 with Technology & Engineering categories.
Accessible and practical framework for machine learning applications and solutions for civil and environmental engineers This textbook introduces engineers and engineering students to the applications of artificial intelligence (AI), machine learning (ML), and machine intelligence (MI) in relation to civil and environmental engineering projects and problems, presenting state-of-the-art methodologies and techniques to develop and implement algorithms in the engineering domain. Through real-world projects like analysis and design of structural members, optimizing concrete mixtures for site applications, examining concrete cracking via computer vision, evaluating the response of bridges to hazards, and predicating water quality and energy expenditure in buildings, this textbook offers readers in-depth case studies with solved problems that are commonly faced by civil and environmental engineers. The approaches presented range from simplified to advanced methods, incorporating coding-based and coding-free techniques. Professional engineers and engineering students will find value in the step-by-step examples that are accompanied by sample databases and codes for readers to practice with. Written by a highly qualified professional with significant experience in the field, Machine Learning includes valuable information on: The current state of machine learning and causality in civil and environmental engineering as viewed through a scientometrics analysis, plus a historical perspective Supervised vs. unsupervised learning for regression, classification, and clustering problems Explainable and causal methods for practical engineering problems Database development, outlining how an engineer can effectively collect and verify appropriate data to be used in machine intelligence analysis A framework for machine learning adoption and application, covering key questions commonly faced by practitioners This textbook is a must-have reference for undergraduate/graduate students to learn concepts on the use of machine learning, for scientists/researchers to learn how to integrate machine learning into civil and environmental engineering, and for design/engineering professionals as a reference guide for undertaking MI design, simulation, and optimization for infrastructure.
Applied Causal Inference
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Author : Uday Kamath
language : en
Publisher: Independently Published
Release Date : 2023-10-06
Applied Causal Inference written by Uday Kamath and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-10-06 with categories.
Recent advancements in causal inference have made it possible to gain profound insight about our world and the complex systems which operate in it. While industry professionals and academics in every domain ask questions of their data, traditional statistical methods often fall short of providing conclusive answers. This is where causality can help. This book gives readers the tools necessary to use causal inference in applied settings by building from theoretical foundations all the way to hands-on case studies in Python. We wrote this book primarily for the practitioner who knows how to work with data but may not be familiar with causal inference concepts, or how to apply those concepts to real-world problems. Part 1 of the book builds from the basic principles of causal inference to the estimation process and into causal discovery, with accompanying exercises and case studies to reinforce concepts. In Parts 2 and 3, we go deeper into cutting-edge applications of causality in machine learning domains, including computer vision, natural language processing, reinforcement learning, and model fairness. The combination of these focuses makes this book a perfect entrypoint into the world of causality for any machine learning professional.
Causal Artificial Intelligence
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Author : Judith S. Hurwitz
language : en
Publisher: John Wiley & Sons
Release Date : 2023-08-23
Causal Artificial Intelligence written by Judith S. Hurwitz and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-08-23 with Computers categories.
Discover the next major revolution in data science and AI and how it applies to your organization In Causal Artificial Intelligence: 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 offers: Clear and compelling descriptions of the concept of causality and how it can benefit your organization Detailed use cases and examples that vividly demonstrate the value of causality for solving business problems Useful strategies for deciding when to use correlation-based approaches and when to use causal inference 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.
Graphical Models And Causal Discovery With Python
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Author : Joe Suzuki
language : en
Publisher: Springer
Release Date : 2026-02-17
Graphical Models And Causal Discovery With Python written by Joe Suzuki and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2026-02-17 with Computers categories.
Beginning with a gentle introduction to causal discovery and the foundations of probability and statistics, this textbook is written in a highly pedagogical way. By uniting probability theory, statistical inference, and graph theory, the book offers a systematic pathway from foundational principles to cutting-edge algorithms, including independence tests, the PC algorithm, LiNGAM, information criteria, and Bayesian methods. Far more than a theoretical treatment, this volume emphasizes hands-on learning through Python implementations, carefully designed exercises with solutions, and intuitive graphical illustrations. Readers will gain the ability to see, run, and understand causal discovery methods in practice. Key features of this book include: A clear and self-contained introduction, bridging probability, statistics, and modern causal discovery techniques 100 exercises with solutions, supporting self-study and classroom use Reproducible Python code, allowing readers to implement and extend the methods themselves Intuitive figures and visual explanations that clarify abstract concepts Broad coverage of applications within statistics and data science, connecting rigorous theory with modern machine learning and causal inference
Causal Inference In Python
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Author : Matheus Facure
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2023-07-14
Causal Inference In Python written by Matheus Facure and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-07-14 with Computers categories.
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. In this book, author Matheus Facure, senior data scientist at Nubank, explains the largely untapped potential of causal inference for estimating impacts and effects. Managers, data scientists, and business analysts will learn classical causal inference methods like randomized control trials (A/B tests), linear regression, propensity score, synthetic controls, and difference-in-differences. Each method is accompanied by an application in the industry to serve as a grounding example. With this book, you will: Learn how to use basic concepts of causal inference Frame a business problem as a causal inference problem Understand how bias gets in the way of causal inference Learn how causal effects can differ from person to person Use repeated observations of the same customers across time to adjust for biases Understand how causal effects differ across geographic locations Examine noncompliance bias and effect dilution
Plug In Estimation Approaches To Causal Inference And Discovery
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Author : Gabriel Ruiz
language : en
Publisher:
Release Date : 2022
Plug In Estimation Approaches To Causal Inference And Discovery written by Gabriel Ruiz and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with categories.
This dissertation covers techniques for the estimation of parameters related to making causal inferences and discoveries. Both for its generality and its simplicity, the focus is in the plug-in estimation of these parameters, whereby the statistical estimator(s) of a parameter(s) is plugged in to obtain an estimator for another, possibly more difficult to estimate, parameter. In particular, the following is addressed. In Chapter 2, we focus on causal discovery, the learning of causality in a data mining scenario. Causal discovery has been of strong scientific and theoretical interest as a starting point to identify ``what causes what?'' Contingent on assumptions and a proper learning algorithm, it is sometimes possible to identify and accurately estimate a causal directed acyclic graph (DAG), as opposed to a Markov equivalence class of graphs that gives ambiguity of causal directions. The focus of this chapter is in highlighting the identifiability and estimation of DAGs with general error distributions through a general sequential sorting procedure that orders variables one at a time, starting at root nodes, followed by children of the root nodes, and so on until completion. We demonstrate a novel application of this general approach to estimate the topological ordering of a DAG. At each step of the procedure, only simple likelihood ratio scores are calculated on regression residuals to decide the next node to append to the current partial ordering. The computational complexity of our algorithm on a p-node problem is O(pd), where d is the maximum neighborhood size. Under mild assumptions, the population version of our procedure provably identifies a true ordering of the underlying DAG. We provide extensive numerical evidence to demonstrate that this sequential procedure scales to possibly thousands of nodes and works well for high-dimensional data. We accompany these numerical experiments with an application to a single-cell gene expression dataset. The focus of the Chapter 3 is the Linear Non-Gaussian Acyclic Model (LiNGAM). Compared to what has been done, we present a novel estimation approach which involves specifying a parametric objective function and arguing when our sequential optimization approach will be statistically consistent, including if the dimension of underlying graph diverges, and when we can provide finite sample guarantees on its accuracy. This involves (1) defining well our target parameter: an ordering of the Directed acyclic graph (DAG)'s vertices such that parents always precede children; and (2) translating deviation bounds on the parameters for the corresponding structural equation model (SEM) into a statement about our topological order estimate's deviation from a true topological ordering. We also incorporate the use of a priori known neighborhood sets to our theoretical results. In Chapter 4, we assume that the underlying causal structure is known, for example, due to the successful application of a causal discovery algorithm similar to those in the previous two chapters. This grants us the identifiability of parameters on the distribution of so-called potential outcomes, the key random variables we would like to make causal claims about. The premise of this chapter, in a vein similar to predictive inference with quantile regression, is that observations may lie far away from their conditional expectation. In the context of causal inference, due to the missing-ness of one outcome, it is difficult to check whether an individual's treatment effect lies close to its prediction given by the estimated Average Treatment Effect (ATE) or Conditional Average Treatment Effect (CATE). With the aim of augmenting the inference with these estimands in practice, we further study an existing distribution-free framework for the plug-in estimation of bounds on the probability an individual benefits from treatment (PIBT), a generally inestimable quantity that would concisely summarize an intervention's efficacy if it could be known. Given the innate uncertainty in the target population-level bounds on PIBT, we seek to better understand the margin of error for the estimation of these target parameters in order to help discern whether estimated bounds on treatment efficacy are tight (or wide) due to random chance or not. In particular, we present non-asymptotic guarantees to the estimation of bounds on marginal PIBT for a randomized experiment setting. We also derive new non-asymptotic results for the case where we would like to understand heterogeneity in PIBT across strata of pre-treatment covariates, with one of our main results in this setting making strategic use of regression residuals. These results, especially those in the randomized experiment case, can be used to help with formal statistical power analyses and frequentist confidence statements for settings where we are interested in inferring PIBT through the target bounds under minimal parametric assumptions. Our results extend to both real-valued and binary-valued outcomes, and these results can also instead be applied to reason about whether an individual is likely to be harmed by an intervention.
Elements Of Causal Inference
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Author : Jonas Peters
language : en
Publisher: MIT Press
Release Date : 2017-12-29
Elements Of Causal Inference written by Jonas Peters and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-12-29 with Computers categories.
A concise and self-contained introduction to causal inference, increasingly important in data science and machine learning. The mathematization of causality is a relatively recent development, and has become increasingly important in data science and machine learning. This book offers a self-contained and concise introduction to causal models and how to learn them from data. After explaining the need for causal models and discussing some of the principles underlying causal inference, the book teaches readers how to use causal models: how to compute intervention distributions, how to infer causal models from observational and interventional data, and how causal ideas could be exploited for classical machine learning problems. All of these topics are discussed first in terms of two variables and then in the more general multivariate case. The bivariate case turns out to be a particularly hard problem for causal learning because there are no conditional independences as used by classical methods for solving multivariate cases. The authors consider analyzing statistical asymmetries between cause and effect to be highly instructive, and they report on their decade of intensive research into this problem. The book is accessible to readers with a background in machine learning or statistics, and can be used in graduate courses or as a reference for researchers. The text includes code snippets that can be copied and pasted, exercises, and an appendix with a summary of the most important technical concepts.
Causal Inference And Causal Discovery With Latent Variables
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Author :
language : en
Publisher:
Release Date :
Causal Inference And Causal Discovery With Latent Variables written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on with categories.