Santiago Acerenza, Julian Martinez-Iriarte, Alejandro Sánchez-Becerra, Pietro Emilio Spini
arXiv 18 Mar 2025 · Econometrics
arXiv:2503.14314 · PDF · Extracted main text
We obtain partial identification of direct and spillover effects in settings with strategic interaction and discrete treatments, outcome and independent instruments. We consider a framework with two decision-makers who play pure-strategy Nash equilibria in treatment take-up, whose outcomes are determined by their joint take-up decisions. We obtain a latent-type representation at the pair level. We enumerate all types that are consistent with pure-strategy Nash equilibria and exclusion restrictions, and then impose conditions such as symmetry, strategic complementarity/substitution, several notions of monotonicity, and homogeneity. Under any combination of the above restrictions, we provide sharp bounds for our parameters of interest via a simple Python optimization routine. Our framework allows the empirical researcher to tailor the above menu of assumptions to their empirical application and to assess their individual and joint identifying power.
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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 | Balke, A. and Pearl, J (1997) Bounds on treatment effects from studies with imperfect compliance | 1.000 | 9 | 3 | 100% |
| 2 | Han, S. and Balat, J (2023) Multiple treatments with strategic substitutes | 1.000 | 5 | 3 | 100% |
| 3 | DiTraglia, F. J., García-Jimeno, C., O’Keeffe-O’Donovan, R., and Sán… (2023) Identifying causal effects in experiments with spillovers and non-compliance self | 0.928 | 4 | 3 | 100% |
| 4 | Kormos, M., Lieli, R. P., and Huber, M (2023) Treatment effect analysis for pairs with endogenous treatment takeup | 0.928 | 4 | 3 | 100% |
| 5 | Kline, B. and Tamer, E (2012) Bounds for best response functions in binary games | 0.894 | 7 | 5 | 71% |
| 6 | Dupas, P., Keats, A., and Robinson, J (2019) The effect of savings accounts on interpersonal financial relationships: Evidence from a field experiment in rural kenya | 0.874 | 5 | 2 | 100% |
| 7 | Vazquez-Bare, G (2023) Causal spillover effects using instrumental variables | 0.814 | 13 | 5 | 54% |
| 8 | Imbens, G. W. and Angrist, J. D (1994) Identification and Estimation of Local Average Treatment Effects | 0.737 | 3 | 2 | 100% |
| 9 | Horowitz, J. L. and Lee, S (2023) Inference in a class of optimization problems: Confidence regions and finite sample bounds on errors in coverage probabilities | 0.693 | 5 | 1 | 100% |
| 10 | Balke, A. and Pearl, J (1994) Counterfactual probabilities: Computational methods, bounds and applications | 0.644 | 4 | 1 | 100% |
Showing the top 10 of 65 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Policy-relevant causal effect estimation using instrumental variables with interference | 0.405 | 1 | 1 |