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Bounding treatment effects: A command for the partial identification of the average treatment effect with endogenous and misreported treatment assignment

Author

Listed:
  • Ian McCarthy

    (Emory University)

  • Daniel L. Millimet

    (Southern Methodist University)

  • Manan Roy

    (University of North Carolina)

Abstract

We present a new command, tebounds, that implements a variety of techniques to bound the average treatment effect of a binary treatment on a binary outcome in light of endogenous and misreported treatment assignment. To tighten the worst case bounds, the monotone treatment selection, monotone treatment response, and monotone instrumental-variable assumptions of Manski and Pepper (2000, Econometrica 68: 997–1010), Kreider and Pepper (2007, Journal of the American Statistical Association 102: 432–441), Kreider et al. (2012, Journal of the American Statistical Association 107: 958–975), and Gundersen, Kreider, and Pepper (2012, Journal of Econometrics 166: 79–91) may be imposed. Imbens– Manski confidence intervals are provided. Copyright 2015 by StataCorp LP.

Suggested Citation

  • Ian McCarthy & Daniel L. Millimet & Manan Roy, 2015. "Bounding treatment effects: A command for the partial identification of the average treatment effect with endogenous and misreported treatment assignment," Stata Journal, StataCorp LP, vol. 15(2), pages 411-436, June.
    • Handle: RePEc:tsj:stataj:v:15:y:2015:i:2:p:411-436
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    Citations

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    Cited by:

    1. Lorenzo Almada & Ian McCarthy & Rusty Tchernis, 2016. "What Can We Learn about the Effects of Food Stamps on Obesity in the Presence of Misreporting?," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 98(4), pages 997-1017.
    2. Husain, Zakir & Ghosh, Saswata & Dutta, Mousumi, 2022. "Changes in dietary practices of mother and child during the COVID-19 lockdown: Results from a household survey in Bihar, India," Food Policy, Elsevier, vol. 112(C).
    3. Brent Kreider & John V. Pepper & Manan Roy, 2020. "Does The Women, Infants, And Children Program Improve Infant Health Outcomes?," Economic Inquiry, Western Economic Association International, vol. 58(4), pages 1731-1756, October.
    4. Millimet, Daniel L. & Roy, Jayjit, 2015. "Multilateral environmental agreements and the WTO," Economics Letters, Elsevier, vol. 134(C), pages 20-23.
    5. Punarjit Roychowdhury & Gaurav Dhamija, 2022. "Don't cross the line: Bounding the causal effect of hypergamy violation on domestic violence in India," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(4), pages 1952-1978, October.
    6. Ian K. McDonough & Constant I. Tra, 2017. "The impact of computer-based tutorials on high school math proficiency," Empirical Economics, Springer, vol. 52(3), pages 1041-1063, May.
    7. Gooch, Elizabeth, 2017. "Estimating the Long-Term Impact of the Great Chinese Famine (1959–61) on Modern China," World Development, Elsevier, vol. 89(C), pages 140-151.
    8. Francesca Molinari, 2020. "Microeconometrics with Partial Identification," Papers 2004.11751, arXiv.org.
    9. Kreider, Brent & Pepper, John V. & Roy, Manan, 2018. "Does the Women, Infants, and Children Program (WIC) Improve Infant Health Outcomes?," ISU General Staff Papers 201805010700001055, Iowa State University, Department of Economics.
    10. Francesca Molinari, 2019. "Econometrics with Partial Identification," CeMMAP working papers CWP25/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    11. Matthew D. Baird & Jonathan Cantor & Wendy M. Troxel & Tamara Dubowitz, 2022. "Job loss and psychological distress during the COVID‐19 pandemic: Longitudinal Analysis from residents in nine predominantly African American low‐income neighborhoods," Health Economics, John Wiley & Sons, Ltd., vol. 31(9), pages 1844-1861, September.
    12. Choudhury, Sanchari, 2019. "WTO membership and corruption," European Journal of Political Economy, Elsevier, vol. 60(C).
    13. Aizawa, T.;, 2019. "Reviewing the Existing Evidence of the Conditional Cash Transfer in India through the Partial Identification Approach," Health, Econometrics and Data Group (HEDG) Working Papers 19/24, HEDG, c/o Department of Economics, University of York.

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