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Statistical and structural modeling represent two distinct approaches to dataanalysis. In this paper, we propose a set of novel methods for combiningstatistical and structural models for improved prediction and causal inference.Our first proposed estimator has the doubly robustness property in that it onlyrequires the correct specification of either the statistical or the structuralmodel. Our second proposed estimator is a weighted ensemble that has theability to outperform both models when they are both misspecified. Experimentsdemonstrate the potential of our estimators in various settings, includingfist-price auctions, dynamic models of entry and exit, and demand estimationwith instrumental variables.

Ensemble Learning with Statistical and Structural Models