A multi-algorithm comparative approach to causal discovery in R

Now that you have a grasp on causal discovery in R, let’s consider a marketing research scenario where you can apply multiple techniques in R.

Unlike the first study, which primarily used Bayesian networks and the PC algorithm, this case study employs multiple methods, including the PC algorithm, the MMHC algorithm, and bootstrapping techniques. This multi-method approach allows for a comparative analysis of different causal discovery techniques, providing a more robust and comprehensive understanding of the causal relationships in the marketing data.

A marketing research firm is conducting a study to understand the complex relationships between demographic factors, digital engagement, brand perception, and consumer behavior in the modern marketplace. They’ve collected data on various aspects of consumer interaction with a brand, and they want to uncover the causal relationships between these factors...