Summary
In this chapter, we guided you through both the theoretical foundations and practical implementation of causal forests using R, particularly in the context of evaluating a social media campaign’s impact on user engagement. We began by explaining the theory behind causal forests, including their ability to estimate HTE and the mathematical formulations involved. After setting up the necessary environment with packages such as grf, tidyverse, and caret, you learned how to create a synthetic dataset representing user demographics and engagement history, prepare this data, and build causal forest models. We then focused on interpreting the results, validating model performance through covariate balance checks and RMSE calculations, and creating insightful visualizations using ggplot2. By following these steps, you were able to understand and estimate the nuanced effects of interventions across different subpopulations, enhancing your data-driven decision-making process.
