arXiv 1 Sep 2020 · Econometrics · publishedJournal of Econometrics (2024) · 7 citations (OpenAlex)
arXiv:2009.00553 · PDF · DOI · OpenAlex · Extracted main text
When a researcher combines multiple instrumental variables for a single binary treatment, the monotonicity assumption of the local average treatment effects (LATE) framework can become restrictive: it requires that all units share a common direction of response even when separate instruments are shifted in opposing directions. What I call vector monotonicity, by contrast, simply assumes treatment uptake to be monotonic in all instruments. I characterize the class of causal parameters that are point identified under vector monotonicity, when the instruments are binary. This class includes, for example, the average treatment effect among units that are in any way responsive to the collection of instruments, or those that are responsive to a given subset of them. The identification results are constructive and yield a simple estimator for the identified treatment effect parameters. An empirical application revisits the labor market returns to college.
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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 | Imbens, Guido W, Angrist, Joshua D (1994) Identification and Estimation of Local Average Treatment Effects | 1.000 | 6 | 5 | 100% |
| 2 | Heckman, James J, Vytlacil, Edward (2005) Structural Equations, Treatment Effects, and Econometric Policy Evaluation | 0.644 | 2 | 2 | 100% |
| 3 | Mogstad, Magne, Santos, Andres, Torgovitsky, Alexander (2018) Using Instrumental Variables for Inference About Policy Relevant Treatment Parameters | 0.511 | 3 | 2 | 33% |
| 4 | (2007) Nonparametric IV estimation of local average treatment effects with covariates | 0.511 | 2 | 2 | 50% |
| 5 | Mogstad, Magne, Torgovitsky, Alexander, Walters, Christopher (2022) Policy Evaluation with Multiple Instrumental Variables | 0.511 | 2 | 2 | 50% |
| 6 | Kisielewicz, A (1988) A solution of Dedekind's problem on the number of isotone Boolean functions. | 0.511 | 2 | 1 | 100% |
| 7 | Anderson, Ian (1987) Combinatorics of finite sets | 0.405 | 1 | 1 | 100% |
| 8 | Angrist, Joshua D (2008) Mostly Harmless Econometrics | 0.405 | 1 | 1 | 100% |
| 9 | Carneiro, Pedro, Heckman, James J., Vytlacil, Edward J (2011) Estimating marginal returns to education | 0.405 | 1 | 1 | 100% |
| 10 | Heckman, James J., Pinto, Rodrigo (2018) Unordered Monotonicity | 0.405 | 1 | 1 | 100% |
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