Statistical approaches to causal analysis

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

[https://us.sagepub.com/en-us/nam/statistical-approaches-to-causal-analysis/book257496#contents]

A practical, up-to-date, step-by-step guidance on causal analysis for advancing students, this volume of the SAGE Quantitative Research kit features worked example datasets throughout to clearly demonstrate the appication of these powerful techniques, giving students the know-how and the confidence to succeed in their quantitative research journey.

Matthew McBee evaluates the issue of causal inference in quantitative research, while providing guidance on how to apply these analyses to your data, discussing key concepts such as:

· Directed acyclic graphs (DAGs)

· Rubin’s Causal Model (RCM)

· Propensity Score Analysis

· Regression Discontinuity Design

(https://us.sagepub.com/en-us/nam/statistical-approaches-to-causal-analysis/book257496)