Unraveling the Complexities of A/B Testing in Marketing Analytics
10 months ago
The article delves into A/B testing in marketing analytics, emphasizing the difficulties of using observational data. It clarifies that randomized experiments are preferred for A/B testing, but sometimes observational data is utilized, potentially leading to incorrect causal inferences. The piece suggests techniques like propensity model matching to equalize treatment and control groups across all confounders, enabling a more precise estimation of the actual causal effect.
