arXiv 23 Jul 2026 · Econometrics
arXiv:2607.21413 · PDF · Extracted main text
Applied econometricians typically model each individual as having fixed outcomes under treatment and control and, in instrumental-variables (IV) settings, fixed treatment decisions under each value of the instrument. This paper asks what changes when outcomes and treatment allocations or choices are stochastic at the individual level. In the model, each individual has a stable (but possibly stochastic) response type consisting of two objects: a treatment choice probability under each state and a potential outcome distribution under each treatment-state pair. These stochastic potential outcomes change the interpretation of some familiar estimators. For instance, in the deterministic IV model, the estimand identifies treatment effect only for compliers - those whose treatment status switches with the instrument. Under stochastic treatment allocation or choice there is no such subgroup: the estimand averages effects over all individuals, weighting each by how much the instrument, policy, or assignment rule moves their probability of treatment. The paper then gives an information-based foundation for stochastic choice, in which individuals act on expected gains given their information.
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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 | Arcidiacono, P., Hotz, V. J., Maurel, A., and Romano, T (2020) Ex ante returns and occupational choice | 0.843 | 3 | 3 | 100% |
| 2 | Blass, A. A., Lach, S., and Manski, C. F (2010) Using elicited choice probabilities to estimate random utility models: Preferences for electricity reliability | 0.843 | 3 | 3 | 100% |
| 3 | Briggs, J. S., Caplin, A., Leth-Petersen, S., and Tonetti, C (2024) Identification of marginal treatment effects using subjective expectations | 0.843 | 3 | 3 | 100% |
| 4 | Angrist, J. D., Imbens, G. W., and Rubin, D. B (1996) Identification of causal effects using instrumental variables | 0.511 | 2 | 1 | 100% |
| 5 | Dawid, A. P (2000) Causal inference without counterfactuals | 0.511 | 2 | 1 | 100% |
| 6 | Imbens, G. W. and Angrist, J. D (1994) Identification and estimation of local average treatment effects | 0.511 | 2 | 1 | 100% |
| 7 | Strzalecki, T (2025) Stochastic Choice Theory | 0.511 | 2 | 1 | 100% |
| 8 | Heckman, J. J., Urzua, S., and Vytlacil, E (2006) Understanding instrumental variables in models with essential heterogeneity | 0.405 | 1 | 1 | 100% |
| 9 | Heckman, J. J. and Vytlacil, E. J (2005) Structural equations, treatment effects, and econometric policy evaluation | 0.405 | 1 | 1 | 100% |
| 10 | Manski, C. F (1990) Nonparametric bounds on treatment effects | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 33 scored citations.