Estimating Cartel Damages Using Machine Learning

30 November 2021
Dr. Oliver März

NERA Consultant Dr. Oliver März authored an article titled “Estimating Cartel Damages Using Machine Learning,” published in the peer-reviewed European Competition Journal. The article presents an alternative to the workhorse linear OLS regression model when predicting “but-for” prices in cartel damages estimation.

By replicating the dataset from a prominent Vitamin C antitrust price-fixing case, Dr. März shows that a supervised machine learning algorithm achieves more accurate predictions of prices based on cross-validated out-of-sample testing than the OLS model applied by the court expert.

Therefore, the machine learning algorithm is better suited in this case to predict prices for the counterfactual scenario that no cartel existed, and to calculate damages based on those predictions. The machine learning algorithm predicts damages 14% lower than those predicted by the court expert’s OLS model. Given that millions of dollars are usually at stake in cartel litigation cases, Dr. März recommends that machine learning algorithms should be in the toolbox of practitioners attempting to derive the most accurate estimate of cartel-related damages.

Dr. März is an expert in cartel damages estimation. The article was published online in the edition of the European Competition Journal released in November 2021.

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