Isaiah Andrews, Drew Fudenberg, Lihua Lei, Annie Liang, Chaofeng Wu
arXiv 10 Feb 2022 · Theoretical Economics · 12 citations (OpenAlex)
arXiv:2202.04796 · PDF · DOI · OpenAlex · Extracted main text
Economists often estimate models using data from a particular domain, e.g. estimating risk preferences in a particular subject pool or for a specific class of lotteries. Whether a model's predictions extrapolate well across domains depends on whether the estimated model has captured generalizable structure. We provide a tractable formulation for this "out-of-domain" prediction problem and define the transfer error of a model based on how well it performs on data from a new domain. We derive finite-sample forecast intervals that are guaranteed to cover realized transfer errors with a user-selected probability when domains are iid, and use these intervals to compare the transferability of economic models and black box algorithms for predicting certainty equivalents. We find that in this application, the black box algorithms we consider outperform standard economic models when estimated and tested on data from the same domain, but the economic models generalize across domains better than the black-box algorithms do.
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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.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Meager, R (2019) Understanding the Average Impact of Microcredit Expansions: A Bayesian Hierarchical Analysis of Seven Randomized Experiments | 0.843 | 3 | 3 | 100% |
| 2 | Meager, R (2022) Aggregating Distributional Treatment Effects: A Bayesian Hierarchical Analysis of the Microcredit Literature | 0.843 | 3 | 3 | 100% |
| 3 | Harless, D. W. and C. F. Camerer (1994) The Predictive Utility of Generalized Expected Utility Theories | 0.811 | 4 | 2 | 100% |
| 4 | Plonsky, O., R. Apel, E. Ert, M. Tennenholtz, D. Bourgin, J. Peterso… (2019) Predicting human decisions with behavioral theories and machine learning | 0.811 | 4 | 2 | 100% |
| 5 | Bruhin, A., H. Fehr-Duda, and T. Epper (2010) Risk and rationality: Uncovering heterogeneity in probability distortion | 0.737 | 4 | 3 | 50% |
| 6 | Hsieh, S.-L., S. Ke, C. Zhao, and Z. Wang (2023) A Logit Neural-Network Utility Model | 0.737 | 3 | 3 | 67% |
| 7 | Bernheim, B. D. and C. Sprenger (2020) On the empirical validity of cumulative prospect theory: Experimental evidence of rank-independent probability weighting | 0.737 | 3 | 3 | 67% |
| 8 | Fudenberg, D., J. Kleinberg, A. Liang, and S. Mullainathan (2022) Measuring the Completeness of Economic Models self | 0.737 | 3 | 2 | 100% |
| 9 | Hey, J. D. and C. Orme (1994) Investigating Generalizations of Expected Utility Theory Using Experimental Data | 0.737 | 3 | 2 | 100% |
| 10 | Peysakhovich, A. and J. Naecker (2017) Using methods from machine learning to evaluate behavioral models of choice under risk and ambiguity | 0.737 | 3 | 2 | 100% |
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