Causal Inference

Causal Inference
ISBN-10
1420076167
ISBN-13
9781420076165
Category
Medical
Pages
352
Language
English
Published
2019-07-07
Publisher
CRC Press
Authors
Miquel A. Hernan, James M. Robins

Description

The application of causal inference methods is growing exponentially in fields that deal with observational data. Written by pioneers in the field, this practical book presents an authoritative yet accessible overview of the methods and applications of causal inference. With a wide range of detailed, worked examples using real epidemiologic data as well as software for replicating the analyses, the text provides a thorough introduction to the basics of the theory for non-time-varying treatments and the generalization to complex longitudinal data.

Other editions

Similar books

  • Elements of Causal Inference: Foundations and Learning Algorithms
    By Bernhard Schölkopf, Jonas Peters, Dominik Janzing

    This book offers a self-contained and concise introduction to causal models and how to learn them from data.

  • Causal Inference in Statistics, Social, and Biomedical Sciences
    By Donald B. Rubin, Guido W. Imbens

    This text presents statistical methods for studying causal effects and discusses how readers can assess such effects in simple randomized experiments.

  • Fundamentals of Causal Inference: With R
    By Babette A. Brumback

    Datasets, R code, and solutions to odd-numbered exercises are available on the book's website at www.routledge.com/9780367705053. Instructors can also find slides based on the book, and a full solutions manual under 'Instructor Resources'.

  • Causal Inference in Statistics: A Primer
    By Nicholas P. Jewell, Judea Pearl, Madelyn Glymour

    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.

  • Causality
    By Judea Pearl

    The book will open the way for including causal analysis in the standard curriculum of statistics, artificial intelligence .

  • Causal Inference in Statistics: A Primer
    By Nicholas P. Jewell, Judea Pearl, Madelyn Glymour

    o We can also calculate the expected value of Y conditional on X, E(Y|X=x), by multiplying each possible value y of Y by P(Y = y|X = x), and summing the products. E(Y|X=x) =Xy P(Y = y/x = x) (1.13) y E(X) is one way to make a “best ...

  • Explanation in Causal Inference: Methods for Mediation and Interaction
    By Tyler J. VanderWeele, Tyler VanderWeele

    Alternative graphical causal models and the identification of direct effects. In: P. Shrout, editor. Causality and Psychopathology: Finding the Determinants of Disorders and Their Cures. Oxford University Press.

  • Statistical Models and Causal Inference: A Dialogue with the Social Sciences
    By David A. Freedman

    The Goldfield-Mantel Stratification procedure is used throughout. Raw Truncated Extensive cases 9/4.41 : 2.04 9/10.2 I 0.88 All cases 21/7.40 : 2.84 21/17.5 : 1.20 Sanctions 29/7.40 : 3.92 29/17.5 : 1.66 ness multiplies the relative ...

  • An Introduction to Causal Inference
    By Judea Pearl

    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 ...

  • The SAGE Handbook of Regression Analysis and Causal Inference
    By Christof Wolf, Henning Best

    ′The editors of the new SAGE Handbook of Regression Analysis and Causal Inference have assembled a wide-ranging, high-quality, and timely collection of articles on topics of central importance to quantitative social research, many written ...