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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 | : |
Publisher | : |
Total Pages | : 0 |
Release | : 2002 |
Genre | : |
ISBN | : 9780262194402 |
Download Cele-6rities Book in PDF, ePub and Kindle
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 | : Clark N. Glymour |
Publisher | : |
Total Pages | : |
Release | : 1999 |
Genre | : Causation |
ISBN | : 9780262315821 |
Download Computation, Causation, and Discovery Book in PDF, ePub and Kindle
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 | : Judea Pearl |
Publisher | : Createspace Independent Publishing Platform |
Total Pages | : 0 |
Release | : 2015 |
Genre | : Causation |
ISBN | : 9781507894293 |
Download An Introduction to Causal Inference Book in PDF, ePub and Kindle
This paper summarizes recent advances in causal inference and underscores the paradigmatic shifts that must be undertaken in moving from traditional statistical analysis to causal analysis of multivariate data. Special emphasis is placed on the assumptions that underly all causal inferences, the languages used in formulating those assumptions, the conditional nature of all causal and counterfactual claims, and the methods that have been developed for the assessment of such claims. These advances are illustrated using a general theory of causation based on the Structural Causal Model (SCM) described in Pearl (2000a), which subsumes and unifies other approaches to causation, and provides a coherent mathematical foundation for the analysis of causes and counterfactuals. In particular, the paper surveys the development of mathematical tools for inferring (from a combination of data and assumptions) answers to three types of causal queries: (1) queries about the effects of potential interventions, (also called "causal effects" or "policy evaluation") (2) queries about probabilities of counterfactuals, (including assessment of "regret," "attribution" or "causes of effects") and (3) queries about direct and indirect effects (also known as "mediation"). Finally, the paper defines the formal and conceptual relationships between the structural and potential-outcome frameworks and presents tools for a symbiotic analysis that uses the strong features of both. The tools are demonstrated in the analyses of mediation, causes of effects, and probabilities of causation. -- p. 1.
Author | : Joseph Y. Halpern |
Publisher | : MIT Press |
Total Pages | : 240 |
Release | : 2016-08-12 |
Genre | : Computers |
ISBN | : 0262035022 |
Download Actual Causality Book in PDF, ePub and Kindle
Explores actual causality, and such related notions as degree of responsibility, degree of blame, and causal explanation. The goal is to arrive at a definition of causality that matches our natural language usage and is helpful, for example, to a jury deciding a legal case, a programmer looking for the line of code that cause some software to fail, or an economist trying to determine whether austerity caused a subsequent depression.
Author | : Judea Pearl |
Publisher | : John Wiley & Sons |
Total Pages | : 162 |
Release | : 2016-01-25 |
Genre | : Mathematics |
ISBN | : 1119186862 |
Download Causal Inference in Statistics Book in PDF, ePub and Kindle
CAUSAL INFERENCE IN STATISTICS A Primer Causality is central to the understanding and use of data. Without an understanding of cause–effect relationships, we cannot use data to answer questions as basic as "Does this treatment harm or help patients?" But though hundreds of introductory texts are available on statistical methods of data analysis, until now, no beginner-level book has been written about the exploding arsenal of methods that can tease causal information from data. Causal Inference in Statistics fills that gap. Using simple examples and plain language, the book lays out how to define causal parameters; the assumptions necessary to estimate causal parameters in a variety of situations; how to express those assumptions mathematically; whether those assumptions have testable implications; how to predict the effects of interventions; and how to reason counterfactually. These are the foundational tools that any student of statistics needs to acquire in order to use statistical methods to answer causal questions of interest. This book is accessible to anyone with an interest in interpreting data, from undergraduates, professors, researchers, or to the interested layperson. Examples are drawn from a wide variety of fields, including medicine, public policy, and law; a brief introduction to probability and statistics is provided for the uninitiated; and each chapter comes with study questions to reinforce the readers understanding.
Author | : Samantha Kleinberg |
Publisher | : Cambridge University Press |
Total Pages | : 269 |
Release | : 2013 |
Genre | : Computers |
ISBN | : 1107026482 |
Download Causality, Probability, and Time Book in PDF, ePub and Kindle
Presents a new approach to causal inference and explanation, addressing both the timing and complexity of relationships.
Author | : Miguel A. Hernan |
Publisher | : |
Total Pages | : 0 |
Release | : 2023-08 |
Genre | : Inference |
ISBN | : 9781315374932 |
Download Causal Inference Book in PDF, ePub and Kindle
"Written by pioneers in the field, this practical book presents an authoritative yet accessible overview of the methods and applications of causal inference. The text provides a thorough introduction to the basics of the theory for non-time-varying treatments and the generalization to complex longitudinal data"--