Matthew A. Masten, Alexandre Poirier
arXiv 29 Apr 2018 · Econometrics
arXiv:1804.10957 · PDF · DOI · OpenAlex · Extracted main text
How should one assess the credibility of assumptions weaker than statistical independence, like quantile independence? In the context of identifying causal effects of a treatment variable, we argue that such deviations should be chosen based on the form of selection on unobservables they allow. For quantile independence, we characterize this form of treatment selection. Specifically, we show that quantile independence is equivalent to a constraint on the average value of either a latent propensity score (for a binary treatment) or the cdf of treatment given the unobservables (for a continuous treatment). In both cases, this average value constraint requires a kind of non-monotonic treatment selection. Using these results, we show that several common treatment selection models are incompatible with quantile independence. We introduce a class of assumptions which weakens quantile independence by removing the average value constraint, and therefore allows for monotonic treatment selection. In a potential outcomes model with a binary treatment, we derive identified sets for the ATT and QTT under both classes of assumptions. In a numerical example we show that the average value constraint inherent in quantile independence has substantial identifying power. Our results suggest that researchers should carefully consider the credibility of this non-monotonicity property when using quantile independence to weaken full independence.
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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, G. W. and W. K. Newey (2009) Identification and estimation of triangular simultaneous equations models without additivity | 0.874 | 5 | 2 | 100% |
| 2 | Chesher, A (2003) Identification in nonseparable models | 0.737 | 3 | 2 | 100% |
| 3 | Manski, C. F (1985) Semiparametric analysis of discrete response: Asymptotic properties of the maximum score estimator | 0.737 | 3 | 2 | 100% |
| 4 | Masten, M. A. and A. Poirier (2018) Identification of treatment effects under conditional partial independence self | 0.721 | 8 | 4 | 38% |
| 5 | Chernozhukov, V. and C. Hansen (2005) An IV model of quantile treatment effects | 0.644 | 2 | 2 | 100% |
| 6 | Imbens, G. W. and J. D. Angrist (1994) Identification and estimation of local average treatment effects | 0.644 | 2 | 2 | 100% |
| 7 | Manski, C. F (1975) Maximum score estimation of the stochastic utility model of choice | 0.644 | 2 | 2 | 100% |
| 8 | Manski, C. F. and J. V. Pepper (2000) Monotone instrumental variables: With an application to the returns to schooling | 0.644 | 2 | 2 | 100% |
| 9 | Nelsen, R. B (2006) An Introduction to Copulas | 0.644 | 2 | 2 | 100% |
| 10 | Manski, C. F (1988) b): Identification of binary response models | 0.585 | 3 | 1 | 100% |
Showing the top 10 of 70 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
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| 1 | Inference on Breakdown Frontiers | 0.405 | 1 | 1 |