Statistical approaches to causal analysis

Table of Contents: 1. Introduction 2. Conditioning 3. Directed Acyclic Graphs 4. Rubin's Causal Model and the Propensity Score 5. Propensity Score Analysis 6. Instrumental Variable Analysis 7. Regression Discontinuity Design 8. Conclusion

This book is a practical, up-to-date, step-by-step guidance on causal analysis; which features worked example datasets throughout to see methods in action. The author clearly demonstrates techniques such as Rubin causal model, direct acyclic graphs and propensity score analysis. It contain guidance on selecting the most appropriate conditioning method for data; understanding directed acyclic graphs and the potential outcomes framework, the unifying principles and language of casual inference; using various techniques and designs, such as propensity score analysis, instrumental variables analysis and regression discontinuity designs, to draw more reliable conclusions from research.