arXiv 11 Jun 2021 · Econometrics · 2 citations (OpenAlex)
arXiv:2106.06421 · PDF · DOI · OpenAlex · Extracted main text
In this paper, we develop a method to assess the sensitivity of local average treatment effect estimates to potential violations of the monotonicity assumption of Imbens and Angrist (1994). We parameterize the degree to which monotonicity is violated using two sensitivity parameters: the first one determines the share of defiers in the population, and the second one measures differences in the distributions of outcomes between compliers and defiers. For each pair of values of these sensitivity parameters, we derive sharp bounds on the outcome distributions of compliers in the first-order stochastic dominance sense. We identify the robust region that is the set of all values of sensitivity parameters for which a given empirical conclusion, e.g. that the local average treatment effect is positive, is valid. Researchers can assess the credibility of their conclusion by evaluating whether all the plausible sensitivity parameters lie in the robust region. We obtain confidence sets for the robust region through a bootstrap procedure and illustrate the sensitivity analysis in an empirical application. We also extend this framework to analyze treatment effects of the entire population.
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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 | Angrist, J. and W. Evans (1998) Children and Their Parents' Labor Supply: Evidence from Exogenous Variation in Family Size | 1.000 | 6 | 3 | 100% |
| 2 | Masten, M. A. and A. Poirier (2020) Inference on Breakdown Frontiers | 0.874 | 12 | 4 | 67% |
| 3 | Stoye, J (2010) Partial identification of spread parameters | 0.843 | 5 | 5 | 60% |
| 4 | Kline, P. and A. Santos (2013) Sensitivity to missing data assumptions: Theory and an evaluation of the U.S | 0.843 | 3 | 3 | 100% |
| 5 | Fang, Z. and A. Santos (2018) Inference on directionally differentiable functions | 0.794 | 6 | 3 | 50% |
| 6 | Angrist, J. D., G. W. Imbens, and D. B. Rubin (1996) Identification of Causal Effects Using Instrumental Variables | 0.737 | 3 | 2 | 100% |
| 7 | De Chaisemartin, C (2017) Tolerating defiance? Local average treatment effects without monotonicity | 0.737 | 3 | 2 | 100% |
| 8 | Kitagawa, T (2021) The identification region of the potential outcome distributions under instrument independence | 0.737 | 3 | 2 | 100% |
| 9 | Hong, H. and J. Li (2018) "The numerical delta method" | 0.644 | 2 | 2 | 100% |
| 10 | Chernozhukov, V., I. Férnandez‐-Val, and A. Galichon (2010) Quantile and Probability Curves Without Crossing | 0.644 | 2 | 2 | 100% |
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