Demystifying Causal Inference

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.

• John Snow and causal inference.- RStudio and R.- Regression and simulation.- Potential outcomes.- Causal graphs.- Experiments.- Matching.- Instrumental Variables.- Regression Discontinuity Design.- Panel Data and fixed effects.- Difference-in-Differences.- Integrating and generalizing causal estimates.