Title: Machine Learning Meets Microeconomics: The Case of Decision Trees and Discrete Choice,
Abstract: We provide a microeconomic framework for decision trees: a popular machine
learning method. Specifically, we show how decision trees represent a
non-compensatory decision protocol known as disjunctions-of-conjunctions and
how this protocol generalizes many of the non-compensatory rules used in the
discrete choice literature so far. Additionally, we show how existing decision
tree variants address many economic concerns that choice modelers might have.
Beyond theoretical interpretations, we contribute to the existing literature of
two-stage, semi-compensatory modeling and to the existing decision tree
literature. In particular, we formulate the first bayesian model tree, thereby
allowing for uncertainty in the estimated non-compensatory rules as well as for
context-dependent preference heterogeneity in one's second-stage choice model.
Using an application of bicycle mode choice in the San Francisco Bay Area, we
estimate our bayesian model tree, and we find that it is over 1,000 times more
likely to be closer to the true data-generating process than a multinomial
logit model (MNL). Qualitatively, our bayesian model tree automatically finds
the effect of bicycle infrastructure investment to be moderated by travel
distance, socio-demographics and topography, and our model identifies
diminishing returns from bike lane investments. These qualitative differences
lead to bayesian model tree forecasts that directly align with the observed
bicycle mode shares in regions with abundant bicycle infrastructure such as
Davis, CA and the Netherlands. In comparison, MNL's forecasts are overly
optimistic.