The econometrics literature proposed several new causal machine learning methods (CML) in the past few years. These methods harness the strength of machine learning methods to flexibly model the relationship between the treatment, outcome and confounders, while providing valid inferential...

This study examines the applicability of modern Data Science techniques in the domain of Strategy. We apply novel techniques from the field of machine learning and text analysis. We proceed in two steps. First, we compare different machine learning techniques to traditional regression methods in...

A novel debate within competition policy and regulation circles is whether autonomous machine learning algorithms may learn to collude on prices. We show that when fims face short-run price commitments, independent Q-learning (a simple but well-established self-learning algorithm) learns to...

When merging firms across large databases in the absence of common identifiers, text algorithms can help. I propose a high-performance fuzzy firm name matching algorithm that uses existing computational methods and works even under hardware restrictions. The algorithm consists of four steps,...

We investigate experimentally whether emotions affect bidding behavior in a firstprice auction. To induce emotions, we confront subjects after a first auction series with apositive or negative random economic shock. We then explore the relation between emotions andbidding behavior in a second...