Impact of social media using causal forests in R

In this section, we explore the practical implementation of causal forests in R to evaluate the impact of a social media campaign on user engagement. The scenario involves creating a synthetic dataset that simulates various user demographics, engagement history, and responses to the campaign. We will use several R packages to facilitate this process, including grf for generalized random forests, tidyverse for data manipulation and visualization, and caret for model training and tuning. The step-by-step process includes setting up the environment, preparing and preprocessing the data, building and tuning the causal forest models, and finally, interpreting and visualizing the results. By following these steps, we aim to derive meaningful insights into the effectiveness of the campaign and inform future decision-making.

Setting up the environment

To implement causal forests in R, we need the following packages:

• grf: This includes...