Vishal Kamat, Samuel Norris, Matthew Pecenco
arXiv 12 Jul 2023 · Econometrics · 2 citations (OpenAlex)
arXiv:2307.06174 · PDF · DOI · OpenAlex · Extracted main text
We develop a method to learn about treatment effects in multiple treatment models with discrete-valued instruments. We allow selection into treatment to be governed by a general class of threshold crossing models that permits multidimensional unobserved heterogeneity. Under a semi-parametric restriction on the distribution of unobserved heterogeneity, we show how a sequence of linear programs can be used to compute sharp bounds for a number of treatment effect parameters when the marginal treatment response functions underlying them remain nonparametric or are additionally parameterized.
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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 | Mogstad, M., Santos, A. and Torgovitsky, A (2018) Using instrumental variables for inference about policy relevant treatment parameters | 1.000 | 12 | 4 | 100% |
| 2 | Lee, S. and Salanié, B (2018) Identifying effects of multivalued treatments | 1.000 | 8 | 3 | 100% |
| 3 | Kline, P. and Walters, C. R (2016) Evaluating public programs with close substitutes: The case of head start | 0.985 | 22 | 6 | 95% |
| 4 | Heckman, J. J. and Vytlacil, E (2005) Structural equations, treatment effects, and econometric policy evaluation 1 | 0.874 | 5 | 2 | 100% |
| 5 | Imbens, G. W. and Angrist, J. D (1994) Identification and estimation of local average treatment effects | 0.843 | 3 | 3 | 100% |
| 6 | Heckman, J. J., Urzua, S. and Vytlacil, E (2006) Understanding instrumental variables in models with essential heterogeneity | 0.811 | 4 | 2 | 100% |
| 7 | Heckman, J. J., Urzua, S. and Vytlacil, E (2008) Instrumental variables in models with multiple outcomes: the general unordered case | 0.737 | 3 | 2 | 100% |
| 8 | Heckman, J. J. and Vytlacil, E. J (1999) Local instrumental variables and latent variable models for identifying and bounding treatment effects | 0.737 | 3 | 2 | 100% |
| 9 | Navjeevan, M., Pinto, R. and Santos, A (2023) Identification and estimation in a class of potential outcomes models | 0.737 | 3 | 2 | 100% |
| 10 | Cox, G. F., Shi, X. and Shimizu, Y (2025) Testing inequalities linear in nuisance parameters | 0.644 | 5 | 2 | 40% |
Showing the top 10 of 60 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | 2SLS with Multiple Treatments | 0.644 | 2 | 2 |
| 2 | Estimating Welfare Effects in a Nonparametric Choice Model: The Case of School Vouchers | 0.405 | 1 | 1 |
| 3 | Treatment Effects with Targeting Instruments | 0.405 | 1 | 1 |
| 4 | A Locally Robust Semiparametric Approach to Examiner IV Designs | 0.405 | 1 | 1 |
| 5 | When does IV identification not restrict outcomes? | 0.405 | 1 | 1 |
| 6 | Sharp Testable Implications of Encouragement Designs | 0.405 | 1 | 1 |