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Treatment Effects With Censoring and Endogeneity

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  • Brigham R. Frandsen

Abstract

This article develops a nonparametric approach to identification and estimation of treatment effects on censored outcomes when treatment may be endogenous and have arbitrarily heterogenous effects. Identification is based on an instrumental variable that satisfies the exclusion and monotonicity conditions standard in the local average treatment effects framework. The article proposes a censored quantile treatment effects estimator, derives its asymptotic distribution, and illustrates its performance using Monte Carlo simulations. Even in the exogenous case, the estimator performs better in finite samples than existing censored quantile regression estimators, and performs nearly as well as maximum likelihood estimators in cases where their distributional assumptions hold. An empirical application to a subsidized job training program finds that participation significantly and dramatically reduced the duration of jobless spells, especially at the right tail of the distribution.

Suggested Citation

  • Brigham R. Frandsen, 2015. "Treatment Effects With Censoring and Endogeneity," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(512), pages 1745-1752, December.
  • Handle: RePEc:taf:jnlasa:v:110:y:2015:i:512:p:1745-1752
    DOI: 10.1080/01621459.2015.1017577
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    Citations

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    Cited by:

    1. Pedro H. C. Sant’Anna, 2021. "Nonparametric Tests for Treatment Effect Heterogeneity With Duration Outcomes," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 39(3), pages 816-832, July.
    2. Linbo Wang & Eric Tchetgen Tchetgen & Torben Martinussen & Stijn Vansteelandt, 2023. "Instrumental variable estimation of the causal hazard ratio," Biometrics, The International Biometric Society, vol. 79(2), pages 539-550, June.
    3. German Blanco & Xuan Chen & Carlos A. Flores & Alfonso Flores-Lagunes, 2020. "Bounds on Average and Quantile Treatment Effects on Duration Outcomes Under Censoring, Selection, and Noncompliance," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 38(4), pages 901-920, October.
    4. Jad Beyhum & Jean-Pierre FLorens & Ingrid Van Keilegom, 2020. "Nonparametric instrumental regression with right censored duration outcomes," Papers 2011.10423, arXiv.org.
    5. Jad Beyhum, 2021. "Two-stage least squares with a randomly right censored outcome," Papers 2110.05107, arXiv.org.
    6. Jad Beyhum & Lorenzo Tedesco & Ingrid Van Keilegom, 2022. "Instrumental variable quantile regression under random right censoring," Papers 2209.01429, arXiv.org, revised Feb 2023.
    7. Jad Beyhum & Jean-Pierre Florens & Ingrid Van Keilegom, 2021. "A nonparametric instrumental approach to endogeneity in competing risks models," Papers 2105.00946, arXiv.org.
    8. Chesher, Andrew & Kim, Dongwoo & Rosen, Adam M., 2023. "IV methods for Tobit models," Journal of Econometrics, Elsevier, vol. 235(2), pages 1700-1724.
    9. Jad Beyhum & Jean-Pierre Florens & Ingrid Keilegom, 2023. "A nonparametric instrumental approach to confounding in competing risks models," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 29(4), pages 709-734, October.
    10. Jing Peng, 2023. "Identification of Causal Mechanisms from Randomized Experiments: A Framework for Endogenous Mediation Analysis," Information Systems Research, INFORMS, vol. 34(1), pages 67-84, March.
    11. Brigham R. Frandsen & Lars J. Lefgren, 2021. "Partial identification of the distribution of treatment effects with an application to the Knowledge is Power Program (KIPP)," Quantitative Economics, Econometric Society, vol. 12(1), pages 143-171, January.
    12. Lv, Xiaofeng & Sun, Xu-Ran & Lu, Yue & Li, Rui, 2019. "Nonparametric identification and estimation of dynamic treatment effects for survival data in a regression discontinuity design," Economics Letters, Elsevier, vol. 184(C).
    13. Beyhum, Jad & Florens, Jean-Pierre & Van Keilegom, Ingrid, 2020. "Nonparametric Instrumental Regression with Right Censored Duration Outcomes," TSE Working Papers 20-1164, Toulouse School of Economics (TSE).

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