Statistical Approaches to Causal Analysis

A practical, up-to-date, step-by-step guidance on causal analysis; which features worked example datasets throughout to see methods in action. McBee clearly demonstrates techniques such as Rubin causal model, direct acyclic graphs and propensity score analysis.

This book provides an up-to-date and accessible introduction to causal inference in quantitative research. Featuring worked example datasets throughout, it clearly outlines the steps involved in carrying out various types of statistical causal analysis. In turn, helping you apply these methods to your own research.

It contains guidance on:

• Selecting the most appropriate conditioning method for your data.
• Applying the Rubin s Causal Model to your analysis, a mathematical framework for understanding and ensuring accurate causation inferences.

• Utilising various techniques and designs, such as propensity scores, instrumental variables analysis, and regression discontinuity designs, to better synthesise and analyse different types of data.
  Part of The SAGE Quantitative Research Kit, this book will give you the know-how and confidence needed to succeed on your quantitative research journey.

  Autorentext

Matthew McBee is a Data Scientist with Eastman Chemical Company (Kingsport, TN, USA). Prior to that, he was a faculty member in the department of psychology at East Tennessee State University (Johnson City, TN, USA) for nine years, where he taught graduate and undergraduate statistics and data analysis courses. He served as a statistician at the Frank Porter Graham Child Development Institute at the University of North Carolina at Chapel Hill. Matthew holds a Ph.D. in Educational Psychology from the University of Georgia.

Inhalt

Introduction
Conditioning
Directed Acyclic Graphs
Rubin s Causal Model and the Propensity Score
Propensity Score Analysis
Instrumental Variable Analysis
Regression Discontinuity Design
Conclusion