Causality, Causal Inference, Machine Learning, Experiment, Data Science.
values before and after this discount. The problem with this method is that it leads to confounded causality. In simple words. it means that we cannot be 100% sure if the before-after change is due to the intervention. Cross-Sectional Comparison, also known as an apple-oranges comparison — This