Estimating Cartel Damages using Machine Learning

This paper argues that the widely applied practice of using OLS regression to predict “but-for” prices for cartel damage estimation is outdated. By replicating the dataset from a prominent Vitamins antitrust case of price-fixing, I show that a supervised machine learning algorithm is objectively better suited than the benchmark OLS model to predict “but-for” prices for the counterfactual scenario that no cartel existed, and to calculate damages based on those predictions. It follows that machine learning algorithms should be in the toolbox of practitioners attempting to derive the most accurate estimate of cartel-related damages