Julián Martínez-Iriarte, Pietro Emilio Spini
arXiv 22 Apr 2022 · Econometrics
arXiv:2204.10445 · PDF · DOI · OpenAlex · Extracted main text
This paper studies the implication of a fraction of the population not responding to the instrument when selecting into treatment. We show that, in general, the presence of non-responders biases the Marginal Treatment Effect (MTE) curve and many of its functionals. Yet, we show that, when the propensity score is fully supported on the unit interval, it is still possible to restore identification of the MTE curve and its functionals with an appropriate re-weighting.
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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 and Vytlacil (2001) Local Instrumental Variables | 0.874 | 6 | 2 | 100% |
| 2 | Acerenza, Ban, and Kedagni (2021) Marginal Treatment Effects with Misclassified Treatment | 0.843 | 3 | 3 | 100% |
| 3 | Heckman and Vytlacil (2005) Structural Equations, Treatment Effects, and Econometric Policy Evaluation | 0.811 | 4 | 2 | 100% |
| 4 | Staiger and Stock (1997) Instrumental Variables Regression with Weak Instruments | 0.737 | 3 | 2 | 100% |
| 5 | Possebom (2021) Crime and Mismeasured Punishment: Marginal Treatment Effect with Misclassification | 0.644 | 2 | 2 | 100% |
| 6 | Bjorklund and Moffitt (1987) The Estimation of Wage Gains and Welfare Gains in Self-Selection | 0.405 | 1 | 1 | 100% |
| 7 | Briggs, Caplin, Leth-Petersen, Tonetti, and Violante (2020) Estimating Marginal Treatment Effects with Survey Instruments | 0.405 | 1 | 1 | 100% |
| 8 | Hahn and Kuersteiner (2002) Discontinuities of weak instrument limiting distributions | 0.405 | 1 | 1 | 100% |
| 9 | Mogstad and Torgovitsky (2018) Identification and Extrapolation of Causal Effects with Instrumental Variables | 0.405 | 1 | 1 | 100% |
| 10 | Heckman, Urzua, and Vytlacil (2006) Understanding Instrumental Variables in Models with Essential Heterogeneity | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 10 scored citations.