Jad Beyhum, Samuele Centorrino, Jean-Pierre Florens, Ingrid Van Keilegom
arXiv 26 Jan 2022 · Mathematics — Statistics Theory · publishedJournal of Business and Economic Statistics (2023) · 4 citations (OpenAlex)
arXiv:2201.10826 · PDF · DOI · OpenAlex · Extracted main text
This paper considers identification and estimation of the causal effect of the time Z until a subject is treated on a survival outcome T. The treatment is not randomly assigned, T is randomly right censored by a random variable C and the time to treatment Z is right censored by min(T,C). The endogeneity issue is treated using an instrumental variable explaining Z and independent of the error term of the model. We study identification in a fully nonparametric framework. We show that our specification generates an integral equation, of which the regression function of interest is a solution. We provide identification conditions that rely on this identification equation. For estimation purposes, we assume that the regression function follows a parametric model. We propose an estimation procedure and give conditions under which the estimator is asymptotically normal. The estimators exhibit good finite sample properties in simulations. Our methodology is applied to find evidence supporting the efficacy of a therapy for burn-out.
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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 | Chernozhukov, V. and Hansen, C (2005) An IV model of quantile treatment effects | 1.000 | 11 | 3 | 100% |
| 2 | Abbring, J. H. and Van den Berg, G. J (2003) The nonparametric identification of treatment effects in duration models | 0.874 | 5 | 2 | 100% |
| 3 | Brown, D. J. and Wegkamp, M. H (2002) Weighted minimum mean-square distance from independence estimation | 0.737 | 4 | 2 | 75% |
| 4 | Van den Berg, G. J., Bonev, P., and Mammen, E (2020) Nonparametric instrumental variable methods for dynamic treatment evaluation | 0.737 | 3 | 2 | 100% |
| 5 | Van den Berg, G. J., Bozio, A., and Costa Dias, M (2020) Policy discontinuity and duration outcomes | 0.644 | 2 | 2 | 100% |
| 6 | Newey, W. K. and Powell, J. L (2003) Instrumental Variable Estimation of Nonparametric Models | 0.585 | 3 | 1 | 100% |
| 7 | Beyhum, J., Florens, J.-P., and Keilegom, I. V (2022) Nonparametric instrumental regression with right censored duration outcomes self | 0.511 | 2 | 1 | 100% |
| 8 | Andrews, D. W. K (2017) Examples of $L^2$-Complete and Boundedly-Complete Distributions | 0.511 | 2 | 1 | 100% |
| 9 | Angrist, J. D., Imbens, G. W., and Rubin, D. B (1996) Identification of causal effects using instrumental variables | 0.511 | 2 | 1 | 100% |
| 10 | Chen, X., Chernozhukov, V., Lee, S., and Newey, W. K (2014) Local identification of nonparametric and semiparametric models | 0.511 | 2 | 1 | 100% |
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