Demystifying Causal Inference

1. Front Matter

   Pages i-xv

   PDF

2. John Snow and Causal Inference

   • Vikram Dayal, Anand Murugesan

   Pages 1-17

3. RStudio and R

   • Vikram Dayal, Anand Murugesan

   Pages 19-31

4. Regression and Simulation

   • Vikram Dayal, Anand Murugesan

   Pages 33-54

5. Potential Outcomes

   • Vikram Dayal, Anand Murugesan

   Pages 55-64

6. Causal Graphs

   • Vikram Dayal, Anand Murugesan

   Pages 65-80

7. Experiments

   • Vikram Dayal, Anand Murugesan

   Pages 81-108

8. Matching

   • Vikram Dayal, Anand Murugesan

   Pages 109-134

9. Instrumental Variables

   • Vikram Dayal, Anand Murugesan

   Pages 135-168

10. Regression Discontinuity Design

    • Vikram Dayal, Anand Murugesan

    Pages 169-192

11. Panel Data and Fixed Effects

    • Vikram Dayal, Anand Murugesan

    Pages 193-226

12. Difference-in-Differences

    • Vikram Dayal, Anand Murugesan

    Pages 227-253

13. Integrating and Generalizing Causal Estimates

    • Vikram Dayal, Anand Murugesan

    Pages 255-294

This book provides an accessible introduction to causal inference and data analysis with R, specifically for a public policy audience. It aims to demystify these topics by presenting them through practical policy examples from a range of disciplines. It provides a hands-on approach to working with data in R using the popular tidyverse package. High quality R packages for specific causal inference techniques like ggdag, Matching, rdrobust, dosearch etc. are used in the book.


The book is in two parts. The first part begins with a detailed narrative about John Snow’s heroic investigations into the cause of cholera. The chapters that follow cover basic elements of R, regression, and an introduction to causality using the potential outcomes framework and causal graphs. The second part covers specific causal inference methods, including experiments, matching, panel data, difference-in-differences, regression discontinuity design, instrumental variables and meta-analysis, with the help of empirical case studies of policy issues. 


The book adopts a layered approach that makes it accessible and intuitive, using helpful concepts, applications, simulation, and data graphs. Many public policy questions are inherently causal, such as the effect of a policy on a particular outcome. Hence, the book would not only be of interest to students in public policy and executive education, but also to anyone interested in analysing data for application to public policy.

• Causal inference
• R
• Simulation
• Regression
• Regression Discontinuity
• Panel Data
• Public policy
• impact evaluation
• Causal graphs
• Experiments
• Matching
• Difference in Difference
• Instrumental variables

• Indian Economic Service Section, Institute of Economic Growth, Delhi, India

  Vikram Dayal

• Central European University, Vienna, Austria

  Anand Murugesan

Vikram Dayal is a Professor at the Institute of Economic Growth, Delhi. He has been using the R software in teaching quantitative economics to diverse audiences and is the author of two popular Springer publications titled An Introduction to R for Quantitative Economics: Graphing, Simulating and Computing, and Quantitative Economics with R: A Data Science Approach.  He has published research on a range of environmental and developmental issues, from outdoor and indoor air pollution in Goa, India, to tigers and Prosopis juliflora in Ranthambore National Park. He studied economics in India and the USA and received his doctoral degree from the Delhi School of Economics, University of Delhi.


Anand Murugesan is an Associate Professor at the Central European University in Vienna. He combines insights from economics and related disciplines with causal inference tools, including lab and lab-in-the-field experiments, and observational data, to study social problems. He holds a Ph.D. from the University of Maryland College Park and studied at the Jawaharlal Nehru University in New Delhi.