arXiv 26 Aug 2026 · Econometrics
arXiv:2608.25720 · PDF · Extracted main text
This paper develops a new econometric framework to identify and estimate policy-relevant causal effects in contexts with endogenous selection into treatment and spillovers within single large networks or spatial settings. Conventional causal inference methods relying on either unconfoundedness or no-interference assumptions are generally inadequate in these scenarios. We introduce a Spillover Roy model that jointly models endogenous treatment selection and potential outcomes while allowing spillovers through a low-dimensional exposure mapping of neighbors' treatments. The model captures heterogeneous treatment responses across levels of latent resistance to treatment and neighborhood exposure. Within this framework, we define policy-relevant direct, spillover, and total effects under feasible policy changes and show that the total effect decomposes into a direct component from policy-induced participation and a spillover component from policy-induced changes in neighborhood treatment exposure. For estimation and inference, we develop a Bayesian data-augmentation algorithm with parameter expansion that enables efficient posterior computation and coherent uncertainty quantification for heterogeneous causal effects and policy counterfactuals. An application to the U.S. Opportunity Zones program finds positive direct effects on housing development but limited spillover benefits, while counterfactual policy analysis reveals diminishing returns from program expansion.
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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 | Heckman, James J and Vytlacil, Edward J (2005) Structural equations, treatment effects, and econometric policy evaluation 1 | 0.811 | 4 | 2 | 100% |
| 2 | Aronow, Peter M and Samii, Cyrus (2017) Estimating average causal effects under general interference, with application to a social network experiment | 0.644 | 2 | 2 | 100% |
| 3 | Carneiro, Pedro and Heckman, James J and Vytlacil, Edward J (2011) Estimating marginal returns to education | 0.644 | 2 | 2 | 100% |
| 4 | Ding, Peng (2014) Bayesian robust inference of sample selection using selection-t models | 0.644 | 2 | 2 | 100% |
| 5 | Do gan, Osman and Ta spinar, Süleyman (2018) Bayesian inference in spatial sample selection models | 0.644 | 2 | 2 | 100% |
| 6 | Manski, Charles F (2013) Identification of treatment response with social interactions | 0.644 | 2 | 2 | 100% |
| 7 | Forastiere, Laura and Airoldi, Edoardo M and Mealli, Fabrizia (2021) Identification and estimation of treatment and interference effects in observational studies on networks | 0.511 | 2 | 1 | 100% |
| 8 | Abbring, Jaap H and Heckman, James J (2007) Econometric evaluation of social programs, part III: Distributional treatment effects, dynamic treatment effects, dynamic discre… | 0.405 | 1 | 1 | 100% |
| 9 | Alm, James and Dronyk-Trosper, Trey and Larkin, Sean (2021) In the land of OZ: designating opportunity zones | 0.405 | 1 | 1 | 100% |
| 10 | Brinch, Christian N and Mogstad, Magne and Wiswall, Matthew (2017) Beyond LATE with a discrete instrument | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 36 scored citations.