Brendan Kline, Matthew A. Masten
arXiv 19 Apr 2025 · Econometrics
arXiv:2504.14127 · PDF · Extracted main text
We develop a new approach for quantifying uncertainty in finite populations, by using design distributions to calibrate sensitivity parameters in finite population identified sets. This yields uncertainty intervals that can be interpreted as identified sets, Bayesian credible sets, or frequentist design-based confidence sets. We focus on quantifying uncertainty about the average treatment effect (ATE) due to missing potential outcomes in a randomized experiment, where our approach (1) yields design-based confidence intervals for ATE which allow for heterogeneous treatment effects but do not rely on asymptotics, (2) provides a new motivation for examining covariate balance, and (3) gives a new formal analysis of the role of randomized treatment assignment. We illustrate our approach in three empirical applications.
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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 | Imbens, Guido W and Donald B Rubin (2015) Causal Inference for Statistics, Social, and Biomedical Sciences | 1.000 | 8 | 5 | 100% |
| 2 | Rambachan, Ashesh and Jonathan Roth (2025) Design-based uncertainty for quasi-experiments | 1.000 | 5 | 3 | 100% |
| 3 | Abadie, Alberto, Susan Athey, Guido W Imbens, and Jeffrey M Wooldridge (2020) Sampling-based versus design-based uncertainty in regression analysis | 0.928 | 4 | 4 | 100% |
| 4 | Chen, Jiafeng, Jonathan Roth, and Jann Spiess (2026) Testing monotonicity in a finite population | 0.874 | 7 | 2 | 100% |
| 5 | Borusyak, Kirill, Peter Hull, and Xavier Jaravel (2025) Design-based identification with formula instruments: a review | 0.874 | 6 | 2 | 100% |
| 6 | Manski, Charles F (2003) Partial Identification of Probability Distributions | 0.874 | 5 | 2 | 100% |
| 7 | Manski, Charles F (1990) Nonparametric bounds on treatment effects | 0.843 | 4 | 3 | 75% |
| 8 | Heckman, James J. and Edward J. Vytlacil (2007) Econometric evaluation of social programs, part I: Causal models, structural models and econometric policy evaluation | 0.811 | 4 | 2 | 100% |
| 9 | Imbens, Guido (2018) Understanding and misunderstanding randomized controlled trials: A commentary on Deaton and Cartwright | 0.811 | 4 | 2 | 100% |
| 10 | Imbens, Guido W. and Joshua D. Angrist (1994) Identification and estimation of local average treatment effects | 0.811 | 4 | 2 | 100% |
Showing the top 10 of 99 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Difference-in-Discontinuities: Estimation, Inference and Validity Tests | 0.405 | 1 | 1 |
| 2 | Breakdown Analysis for Instrumental Variables with Binary Outcomes | 0.405 | 1 | 1 |
| 3 | Testing Monotonicity in a Finite Population | 0.405 | 1 | 1 |
| 4 | Randomization Inference For the Always-Reporter Average Treatment Effect | 0.405 | 1 | 1 |