  Choice modeling is at the core of understanding how changes to the
competitive landscape affect consumer choices and reshape market equilibria. In
this paper, we propose a fundamental characterization of choice functions that
encompasses a wide variety of extant choice models. We demonstrate how
non-parametric estimators like neural nets can easily approximate such
functionals and overcome the curse of dimensionality that is inherent in the
non-parametric estimation of choice functions. We demonstrate through extensive
simulations that our proposed functionals can flexibly capture underlying
consumer behavior in a completely data-driven fashion and outperform
traditional parametric models. As demand settings often exhibit endogenous
features, we extend our framework to incorporate estimation under endogenous
features. Further, we also describe a formal inference procedure to construct
valid confidence intervals on objects of interest like price elasticity.
Finally, to assess the practical applicability of our estimator, we utilize a
real-world dataset from S. Berry, Levinsohn, and Pakes (1995). Our empirical
analysis confirms that the estimator generates realistic and comparable own-
and cross-price elasticities that are consistent with the observations reported
in the existing literature.
