Ensemble Learning with Statistical and Structural Models

TL;DR

This paper introduces novel ensemble methods combining statistical and structural models, enhancing prediction accuracy and causal inference, with robustness to model misspecification demonstrated through various experiments.

Contribution

It proposes two new ensemble estimators: one with doubly robustness and another that outperforms individual models when both are misspecified.

Findings

Doubly robust estimator requires only one correct model

Weighted ensemble outperforms individual models under misspecification

Experiments show effectiveness in auctions, entry/exit models, and demand estimation

Abstract

Statistical and structural modeling represent two distinct approaches to data analysis. In this paper, we propose a set of novel methods for combining statistical and structural models for improved prediction and causal inference. Our first proposed estimator has the doubly robustness property in that it only requires the correct specification of either the statistical or the structural model. Our second proposed estimator is a weighted ensemble that has the ability to outperform both models when they are both misspecified. Experiments demonstrate the potential of our estimators in various settings, including fist-price auctions, dynamic models of entry and exit, and demand estimation with instrumental variables.