Deep Learning for Individual Heterogeneity
Abstract/Contents
- Abstract
- We propose a methodology for effectively modeling individual heterogeneity using deep learning while still retaining the interpretability and economic discipline of classical models. We pair a transparent, interpretable modeling structure with rich data environments and machine learning methods to estimate heterogeneous parameters based on potentially high dimensional or complex observable characteristics. Our framework is widely-applicable, covering numerous settings of economic interest. We recover, as special cases, well-known examples such as average treatment effects and parametric components of partially linear models. However, we also seamlessly deliver new results for diverse examples such as price elasticities, willingness-to-pay, and surplus measures in choice models, average marginal and partial effects of continuous treatment variables, fractional outcome models, count data, heterogeneous production function components, and more. Deep neural networks are particularly well-suited to structured modeling of heterogeneity in economics: we show how the network architecture can be easily designed to match the global structure of the economic model, giving novel methodology for deep learning as well as, more formally, improved rates of convergence. Our results on deep learning have consequences for other structured modeling environments and applications, such as for additive models or other varying coefficient models. Our inference results are based on an influence function we derive, which we show to be flexible enough to to encompass all settings with a single, unified calculation, removing any requirement for case-by-case derivations. The usefulness of the methodology in economics is shown in two empirical applications: we study the response of 410(k) participation rates to firm matching and the impact of prices on subscription choices for an online service. Extensions of the main ideas to instrumental variables and multinomial choices are shown.
Description
| Type of resource | text |
|---|---|
| Date created | July 12, 2021 |
Creators/Contributors
| Author | Farrell, Max H. |
|---|---|
| Author | Liang, Tengyuan |
| Author | Misra, Sanjog |
| Organizer of meeting | Santos, Andres |
| Organizer of meeting | Shaikh, Azeem |
| Organizer of meeting | Wolak, Frank |
Subjects
| Subject | heterogeneity |
|---|---|
| Subject | deep learning |
| Subject | doubly robust |
| Subject | semiparametric inference |
| Subject | varying coeffcient model |
| Subject | structural models |
| Genre | Text |
| Genre | Working paper |
| Genre | Grey literature |
Bibliographic information
Access conditions
- Use and reproduction
- User agrees that, where applicable, content will not be used to identify or to otherwise infringe the privacy or confidentiality rights of individuals. Content distributed via the Stanford Digital Repository may be subject to additional license and use restrictions applied by the depositor.
- License
- This work is licensed under a Creative Commons Attribution 4.0 International license (CC BY).
Preferred citation
- Preferred citation
- Farrell, M., Liang, T., and Misra, S. (2021). Deep Learning for Individual Heterogeneity. Stanford Digital Repository. Available at https://purl.stanford.edu/bk477qs5662
Collection
SITE Conference 2021
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- siteworkshop@stanford.edu
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