Sokbae Lee, Bernard Salanié
arXiv 20 Jul 2020 · Econometrics
arXiv:2007.10432 · PDF · Extracted main text
Multivalued treatments are commonplace in applications. We explore the use of discrete-valued instruments to control for selection bias in this setting. Our discussion revolves around the concept of targeting: which instruments target which treatments. It allows us to establish conditions under which counterfactual averages and treatment effects are point- or partially-identified for composite complier groups. We illustrate the usefulness of our framework by applying it to data from the Head Start Impact Study. Under a plausible positive selection assumption, we derive informative bounds that suggest less beneficial effects of Head Start expansions than the parametric estimates of Kline and Walters (2016).
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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 | Kline and Walters (2016) Evaluating public programs with close substitutes: The case of Head Start | 0.960 | 26 | 4 | 88% |
| 2 | Kirkeboen, Leuven, and Mogstad (2016) Field of study, earnings, and self-selection | 0.941 | 6 | 4 | 83% |
| 3 | Heckman and Pinto (2018) Unordered Monotonicity | 0.928 | 15 | 5 | 80% |
| 4 | Bai, Huang, and Tabord-Meehan (2025) Sharp Testable Implications of Encouragement Designs | 0.928 | 4 | 3 | 100% |
| 5 | Heckman and Vytlacil (2007) Econometric Evaluation of Social Programs, Part II: Using the Marginal Treatment Effect to Organize Alternative Econometric Esti… | 0.874 | 5 | 2 | 100% |
| 6 | Mogstad, Torgovitsky, and Walters (2021) The Causal Interpretation of Two-Stage Least Squares with Multiple Instrumental Variables | 0.843 | 3 | 3 | 100% |
| 7 | Angrist and Imbens (1995) Two-stage least squares estimation of average causal effects in models with variable treatment intensity | 0.843 | 3 | 3 | 100% |
| 8 | Pinto (2022) Beyond Intention to Treat: Using the Incentives in Moving to Opportunity to Identify Neighborhood Effects | 0.754 | 7 | 3 | 43% |
| 9 | Bai, Huang, Moon, Shaikh, and Vytlacil (2024) On the Identifying Power of Monotonicity for Average Treatment Effects | 0.737 | 3 | 2 | 100% |
| 10 | Angrist, Santos, and Tecchio (2025) One Instrument, Many Treatments: Instrumental Variables Identification of Multiple Causal Effects | 0.644 | 2 | 2 | 100% |
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