Case study – Heterogeneity in R

This case study aims to illustrate the application of various R programming techniques and methodologies in analyzing heterogeneity in causal inference, particularly in the context of selling bicycles to a diverse group of customers. The scenario encompasses multiple factors affecting bicycle sales, including purposes of biking (sports, commuting, carrying heavy items, occasional biking, city biking, rural biking), as well as demographics (age), and environmental conditions (price, weather, road conditions). The goal is to generate synthetic data that mimics this complex scenario and apply advanced statistical methods to understand the causal impact of different factors on bicycle sales. You can learn more about a similar study in R shown here [7]:

packages <- c("tidyverse", "caret", "MatchIt", "panelMatch", "ggplot2", "synthpop", "fixest", "dplyr", "lubridate...