Discovering Causal Structure PDF Download
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Author | : Clark Glymour |
Publisher | : Academic Press |
Total Pages | : 413 |
Release | : 2014-05-10 |
Genre | : Social Science |
ISBN | : 148326579X |
Download Discovering Causal Structure Book in PDF, ePub and Kindle
Discovering Causal Structure: Artificial Intelligence, Philosophy of Science, and Statistical Modeling provides information pertinent to the fundamental aspects of a computer program called TETRAD. This book discusses the version of the TETRAD program, which is designed to assist in the search for causal explanations of statistical data. or alternative models. This text then examines the notion of applying artificial intelligence methods to problems of statistical model specification. Other chapters consider how the TETRAD program can help to find god alternative models where they exist, and how it can help detect the existence of important neglected variables. This book discusses as well the procedures for specifying a model or models to account for non-experimental or quasi-experimental data. The final chapter presents a description of the format of input files and a description of each command. This book is a valuable resource for social scientists and researchers.
Author | : Clark N. Glymour |
Publisher | : |
Total Pages | : 424 |
Release | : 1987 |
Genre | : Computers |
ISBN | : |
Download Discovering Causal Structure Book in PDF, ePub and Kindle
Designed to assist in the search of casual explanations of statistical datas.
Author | : |
Publisher | : |
Total Pages | : |
Release | : 1987 |
Genre | : |
ISBN | : |
Download Discovering causal structure Book in PDF, ePub and Kindle
Author | : |
Publisher | : |
Total Pages | : 394 |
Release | : 1987 |
Genre | : Artificial intelligence |
ISBN | : 9780122869624 |
Download Discovering causal structure Book in PDF, ePub and Kindle
Designed to assist in the search of casual explanations of statistical datas.
Author | : Peter Spirtes |
Publisher | : Springer Science & Business Media |
Total Pages | : 551 |
Release | : 2012-12-06 |
Genre | : Mathematics |
ISBN | : 1461227488 |
Download Causation, Prediction, and Search Book in PDF, ePub and Kindle
This book is intended for anyone, regardless of discipline, who is interested in the use of statistical methods to help obtain scientific explanations or to predict the outcomes of actions, experiments or policies. Much of G. Udny Yule's work illustrates a vision of statistics whose goal is to investigate when and how causal influences may be reliably inferred, and their comparative strengths estimated, from statistical samples. Yule's enterprise has been largely replaced by Ronald Fisher's conception, in which there is a fundamental cleavage between experimental and non experimental inquiry, and statistics is largely unable to aid in causal inference without randomized experimental trials. Every now and then members of the statistical community express misgivings about this turn of events, and, in our view, rightly so. Our work represents a return to something like Yule's conception of the enterprise of theoretical statistics and its potential practical benefits. If intellectual history in the 20th century had gone otherwise, there might have been a discipline to which our work belongs. As it happens, there is not. We develop material that belongs to statistics, to computer science, and to philosophy; the combination may not be entirely satisfactory for specialists in any of these subjects. We hope it is nonetheless satisfactory for its purpose.
Author | : Chandler B. Squires |
Publisher | : |
Total Pages | : 44 |
Release | : 2019 |
Genre | : |
ISBN | : |
Download Causal Structure Discovery from Incomplete Data Book in PDF, ePub and Kindle
Causal structure learning is a fundamental tool for building a scientific understanding of the way a system works. However, in many application areas, such as genomics, the information necessary for current causal structure learning algorithms does not match the information that researchers can actually access, for example when the algorithm requires knowledge of intervention targets but the interventions have off-target effects. In this thesis, we developed, implemented, and tested a novel algorithm for discovering a causal DAG from observational and interventional data, when the intervention targets are either partially or completely unknown. We relate the algorithm to the recently introduced Joint Causal Inference framework. Finally, we evaluate the performance of the algorithm on synthetic datasets and demonstrated its ability to outperform current state-of-the-art causal structure learning algorithms which assume known intervention targets.
Author | : Clark N. Glymour |
Publisher | : |
Total Pages | : 576 |
Release | : 1999 |
Genre | : Business & Economics |
ISBN | : |
Download Computation, Causation, and Discovery Book in PDF, ePub and Kindle
In science, business, and policymaking -- anywhere data are used in prediction -- two sorts of problems requiring very different methods of analysis often arise. The first, problems of recognition and classification, concerns learning how to use some features of a system to accurately predict other features of that system. The second, problems of causal discovery, concerns learning how to predict those changes to some features of a system that will result if an intervention changes other features. This book is about the second -- much more difficult -- type of problem. Typical problems of causal discovery are: How will a change in commission rates affect the total sales of a company? How will a reduction in cigarette smoking among older smokers affect their life expectancy? How will a change in the formula a college uses to award scholarships affect its dropout rate? These sorts of changes are interventions that directly alter some features of the system and perhaps -- and this is the question -- indirectly alter others. The contributors discuss recent research and applications using Bayes nets or directed graphic representations, including representations of feedback or recursive systems. The book contains a thorough discussion of foundational issues, algorithms, proof techniques, and applications to economics, physics, biology, educational research, and other areas.
Author | : Jonas Peters |
Publisher | : MIT Press |
Total Pages | : 289 |
Release | : 2017-11-29 |
Genre | : Computers |
ISBN | : 0262037319 |
Download Elements of Causal Inference Book in PDF, ePub and Kindle
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.
Author | : Thomas S. Richardson |
Publisher | : |
Total Pages | : 42 |
Release | : 1996 |
Genre | : Causation |
ISBN | : |
Download Discovering Cyclic Causal Structure Book in PDF, ePub and Kindle
Abstract: "This paper is concerned with the problem of making causal inferences from observational data, when the underlying causal structure may involve feedback loops. In particular, making causal inferences under the assumption that the causal system which generated the data is linear and that there are no unmeasured common causes (latent variables). Linear causal structures of this type can be represented by non-recursive linear structural equation models. I present a correct polynomial time (on sparse graphs) discovery algorithm for linear cyclic models that contain no latent variables. This algorithm outputs a representation of a class of non-recursive linear structural equation models, given observational data as input. Under the assumption that all conditional independencies found in the observational data are true for structural reasons rather than because of particular parameter values, the algorithm discovers causal features of the structure which generated the data. A simple modification of the algorithm can be used as a decision procedure (whose runtime is polynomial in the number of vertices) for determining when two directed graphs (cyclic or acyclic) entail the same set of conditional independence relations. After proving that the algorithm is correct I then show that it is also complete in the sense that if two linear structural equation models are used as conditional independence 'oracles' for the discovery algorithm, then the algorithm will give the same output only if every conditional independence entailed by one model is entailed by the other and vice versa. Another way of saying this is that the algorithm can be used as a decision procedure for determining Markov equivalence of directed cyclic graphs; if the conditional independencies associated with two cyclic graphs result in the same output from the algorithm, when used as input, then the two graphs are equivalent."
Author | : Aleksander Molak |
Publisher | : Packt Publishing Ltd |
Total Pages | : 456 |
Release | : 2023-05-31 |
Genre | : Computers |
ISBN | : 1804611735 |
Download Causal Inference and Discovery in Python Book in PDF, ePub and Kindle
Demystify causal inference and casual discovery by uncovering causal principles and merging them with powerful machine learning algorithms for observational and experimental data Purchase of the print or Kindle book includes a free PDF eBook 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.