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Treatment Effect Bounds: An Application to Swan-Ganz Catheterization

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  • Jay Bhattacharya
  • Azeem Shaikh
  • Edward Vytlacil

Abstract

We reanalyze data from the observational study by Connors et al. (1996) on the impact of Swan-Ganz catheterization on mortality outcomes. The Connors et al. (1996) study assumes that there are no unobserved differences between patients who are catheterized and patients who are not catheterized and finds that catheterization increases patient mortality. We instead allow for such differences between patients by implementing both the bounds of Manski (1990), which only exploits an instrumental variable, and the bounds of Shaikh and Vytlacil (2004), which exploit mild nonparametric, structural assumptions in addition to an instrumental variable. We propose and justify the use of indicators of weekday admission as an instrument for catheterization in this context. We find that in our application, the Manski (1990) bounds do not indicate whether catheterization increases or decreases mortality, whereas the Shaikh and Vytlacil (2004) bounds reveal that catheterization increases mortality at 30 days and beyond. We also extend the analysis of Shaikh and Vytlacil (2004) to exploit a further nonparametric, structural assumption -- that doctors catheterize individuals with systematically worse latent health -- and find that this assumption further narrows these bounds and strengthens our conclusions.

Suggested Citation

  • Jay Bhattacharya & Azeem Shaikh & Edward Vytlacil, 2005. "Treatment Effect Bounds: An Application to Swan-Ganz Catheterization," NBER Working Papers 11263, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:11263
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    Cited by:

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    5. Kyunghoon Ban & Désiré Kédagni, 2022. "Nonparametric bounds on treatment effects with imperfect instruments [Instrument-based estimation with binarized treatments: Issues and tests for the exclusion restriction]," The Econometrics Journal, Royal Economic Society, vol. 25(2), pages 477-493.
    6. Lina Zhang & David T. Frazier & Don S. Poskitt & Xueyan Zhao, 2020. "Decomposing Identification Gains and Evaluating Instrument Identification Power for Partially Identified Average Treatment Effects," Monash Econometrics and Business Statistics Working Papers 34/20, Monash University, Department of Econometrics and Business Statistics.
    7. Marc Henry & Ismael Mourifié, 2012. "Sharp Bounds in the Binary Roy Model," CIRJE F-Series CIRJE-F-835, CIRJE, Faculty of Economics, University of Tokyo.
    8. Jay Bhattacharya & Adam Isen, 2008. "On Inferring Demand for Health Care in the Presence of Anchoring, Acquiescence, and Selection Biases," NBER Working Papers 13865, National Bureau of Economic Research, Inc.
    9. Domenico Depalo & Santiago Pereda-Fernández, 2020. "Consistent estimates of the public/private wage gap," Empirical Economics, Springer, vol. 58(6), pages 2937-2947, June.
    10. John A. List & Azeem M. Shaikh & Yang Xu, 2019. "Multiple hypothesis testing in experimental economics," Experimental Economics, Springer;Economic Science Association, vol. 22(4), pages 773-793, December.
    11. Sung Jae Jun & Sokbae (Simon) Lee, 2020. "Causal inference in case-control studies," CeMMAP working papers CWP19/20, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    12. Domenico Depalo, 2021. "True COVID-19 mortality rates from administrative data," Journal of Population Economics, Springer;European Society for Population Economics, vol. 34(1), pages 253-274, January.
    13. Francesca Molinari, 2020. "Microeconometrics with Partial Identification," Papers 2004.11751, arXiv.org.
    14. Chen, Xuan & Flores, Carlos A. & Flores-Lagunes, Alfonso, 2015. "Going Beyond LATE: Bounding Average Treatment Effects of Job Corps Training," IZA Discussion Papers 9511, Institute of Labor Economics (IZA).
    15. Michael Gerfin & Martin Schellhorn, 2006. "Nonparametric bounds on the effect of deductibles in health care insurance on doctor visits – Swiss evidence," Health Economics, John Wiley & Sons, Ltd., vol. 15(9), pages 1011-1020, September.
    16. Magne Mogstad & Andres Santos & Alexander Torgovitsky, 2018. "Using Instrumental Variables for Inference About Policy Relevant Treatment Parameters," Econometrica, Econometric Society, vol. 86(5), pages 1589-1619, September.
    17. 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.
    18. Francesca Molinari, 2019. "Econometrics with Partial Identification," CeMMAP working papers CWP25/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    19. Machado, Cecilia & Shaikh, Azeem M. & Vytlacil, Edward J., 2019. "Instrumental variables and the sign of the average treatment effect," Journal of Econometrics, Elsevier, vol. 212(2), pages 522-555.
    20. John A. List & Azeem M. Shaikh & Atom Vayalinkal, 2023. "Multiple testing with covariate adjustment in experimental economics," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(6), pages 920-939, September.
    21. Possebom, Vitor, 2018. "Sharp bounds on the MTE with sample selection," MPRA Paper 89785, University Library of Munich, Germany.
    22. Martin Huber & Giovanni Mellace, 2010. "Sharp IV bounds on average treatment effects under endogeneity and noncompliance," University of St. Gallen Department of Economics working paper series 2010 2010-31, Department of Economics, University of St. Gallen.
    23. Jay Bhattacharya & William B. Vogt, 2007. "Do Instrumental Variables Belong in Propensity Scores?," NBER Technical Working Papers 0343, National Bureau of Economic Research, Inc.
    24. Sokbae Lee & Martin Weidner, 2021. "Bounding Treatment Effects by Pooling Limited Information across Observations," Papers 2111.05243, arXiv.org, revised Dec 2023.

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    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • I1 - Health, Education, and Welfare - - Health

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