Jiafeng Chen, Lihua Lei, Timothy Sudijono, Liyang Sun, Tian Xie
arXiv 14 Nov 2025 · Econometrics
arXiv:2511.11862 · PDF · DOI · OpenAlex · Extracted main text
This paper proposes methods for producing compound selection decisions in a Gaussian sequence model. Given unknown, fixed parameters $μ_ {1:n}$ and known $σ_{1:n}$ with observations $Y_i \sim \textsf{N}(μ_i, σ_i^2)$, the decision maker would like to select a subset of indices $S$ so as to maximize utility $\frac{1}{n}\sum_{i\in S} (μ_i - K_i)$, for known costs $K_i$. Inspired by Stein's unbiased risk estimate (SURE), we introduce an almost unbiased estimator, called ASSURE, for the expected utility of a proposed decision rule. ASSURE allows a user to choose a welfare-maximizing rule from a pre-specified class by optimizing the estimated welfare, thereby producing selection decisions that borrow strength across noisy estimates. We show that ASSURE produces decision rules that are asymptotically no worse than the optimal but infeasible decision rule in the pre-specified class. We apply ASSURE to the selection of Census tracts for economic opportunity, the identification of discriminating firms, and the analysis of $p$-value decision procedures in A/B testing.
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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 | Kitagawa, Toru and Tetenov, Aleksey (2018) Who should be treated? empirical welfare maximization methods for treatment choice | 1.000 | 6 | 3 | 100% |
| 2 | Liang, TaChen (2000) On an empirical Bayes test for a normal mean | 1.000 | 5 | 3 | 100% |
| 3 | Sudijono, Timothy and Ejdemyr, Simon and Lal, Apoorva and Tingley, M… (2024) Optimizing returns from experimentation programs self | 1.000 | 5 | 3 | 100% |
| 4 | Chen, Jiafeng (2025) Empirical Bayes when estimation precision predicts parameters self | 0.959 | 17 | 6 | 88% |
| 5 | Chetty, Raj and Friedman, John N and Hendren, Nathaniel and Jones, M… (2018) The opportunity atlas: Mapping the childhood roots of social mobility | 0.941 | 6 | 4 | 83% |
| 6 | Azevedo, Eduardo M and Deng, Alex and Montiel Olea, José Luis and Ra… (2020) A/b testing with fat tails | 0.941 | 6 | 3 | 83% |
| 7 | Bergman, Peter and Chetty, Raj and DeLuca, Stefanie and Hendren, Nat… (2024) Creating moves to opportunity: Experimental evidence on barriers to neighborhood choice | 0.928 | 10 | 4 | 80% |
| 8 | Jiang, Wenhua (2020) On general maximum likelihood empirical Bayes estimation of heteroscedastic IID normal means | 0.928 | 4 | 3 | 100% |
| 9 | Liang, Ta Chen (2004) On optimal convergence rate of empirical Bayes tests | 0.928 | 4 | 3 | 100% |
| 10 | Kline, Patrick and Rose, Evan K and Walters, Christopher R (2022) Systemic discrimination among large US employers | 0.874 | 8 | 2 | 100% |
Showing the top 10 of 84 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Compound Estimation for Binomials | 0.737 | 3 | 2 |
| 2 | Assumption-Lean Shrinkage and Model Averaging for Spatial Parameters | 0.693 | 6 | 4 |