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Ensemble Learning with Statistical and Structural Models

Jiaming Mao, Jingzhi Xu

arXiv 7 Jun 2020 · Econometrics · 1 citations (OpenAlex)

arXiv:2006.05308 · PDF · DOI · OpenAlex · Extracted main text

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.

Citation extraction

104
references
147
in-text mentions
104
distinct cited
1
self-citations
17,255
main-text words

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Most heavily cited references

The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.

ReferenceIntensityMentionsSectionsMain text
1Hansen, B. E. and Racine, J. S (2012) Jackknife model averaging0.87462100%
2Lewbel, A., Choi, J.-Y., and Zhou, Z (2019) General Doubly Robust Identification and Estimation0.87452100%
3Wolpert, D. H (1992) Stacked generalization0.87452100%
4Ando, T. and Li, K.-C (2017) A weight-relaxed model averaging approach for high-dimensional generalized linear models0.73732100%
5Athey, S., Tibshirani, J., and Wager, S (2019) Generalized random forests0.73732100%
6Chernozhukov, V., Chetverikov, D., Demirer, M., Duflo, E., Hansen, C… (2016) Double machine learning for treatment and causal parameters0.73732100%
7Chetty, R (2009) Sufficient Statistics for Welfare Analysis: A Bridge Between Structural and Reduced-Form Methods0.69351100%
8Breiman, L (2001) Random forests0.64422100%
9Chernozhukov, V., Chetverikov, D., Demirer, M., Duflo, E., Hansen, C… (2017) Double/debiased/neyman machine learning of treatment effects0.64422100%
10Pearl, J (2009) Causality0.64422100%

Showing the top 10 of 104 scored citations.

Cited by, within the corpus

arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.

Citing paperIntensityMentionsSections
1Structural Regularization0.40511