2020/04/08 by Daniel Björkegren, Björkegren, Daniel, Joshua E. Blumenstock +3 · 1 citation
Computer Science · Economics, Econometrics and Finance · #Econometrics (econ.EM) #FOS: Computer and information sciences #FOS: Economics and business #Machine Learning (cs.LG) #Theoretical Economics (econ.TH) #cs.LG #econ.EM #econ.TH
paper · pdf · doi:10.48550/arxiv.2004.03865
arxiv created 2020/04/08 · arxiv updated 2020/04/09
An increasing number of decisions are guided by machine learning algorithms. In many settings, from consumer credit to criminal justice, those decisions are made by applying an estimator to data on an individual's observed behavior. But when consequential decisions are encoded in rules, individuals may strategically alter their behavior to achieve desired outcomes. This paper develops a new class of estimator that is stable under manipulation, even when the decision rule is fully transparent. We explicitly model the costs of manipulating different behaviors, and identify decision rules that are stable in equilibrium. Through a large field experiment in Kenya, we show that decision rules estimated with our strategy-robust method outperform those based on standard supervised learning approaches.