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Can Nonexperimental Methods Provide Unbiased Estimates of a Breastfeeding Intervention? A Within-Study Comparison of Peer Counseling in Oregon

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  • Onur Altindag
  • Theodore J. Joyce
  • Julie A. Reeder

Abstract

Between July 2005 and July 2007, the Oregon Supplemental Nutrition Program for Women, Infants and Children program conducted the largest randomized field experiment (RFE) ever in the United States to assess the effectiveness of a low-cost peer counseling intervention to promote exclusive breastfeeding. We undertook a within-study comparison of the intervention using unique administrative data between July 2005 and July 2010. We found no difference between experimental and nonexperimental estimates but failed to determine correspondence based on more stringent criteria. We show that tests for nonconsent bias in the benchmark RFE might provide an important signal as to confounding in the nonexperimental estimates.

Suggested Citation

  • Onur Altindag & Theodore J. Joyce & Julie A. Reeder, 2019. "Can Nonexperimental Methods Provide Unbiased Estimates of a Breastfeeding Intervention? A Within-Study Comparison of Peer Counseling in Oregon," Evaluation Review, , vol. 43(3-4), pages 152-188, June.
  • Handle: RePEc:sae:evarev:v:43:y:2019:i:3-4:p:152-188
    DOI: 10.1177/0193841X19865963
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    References listed on IDEAS

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    1. LaLonde, Robert J, 1986. "Evaluating the Econometric Evaluations of Training Programs with Experimental Data," American Economic Review, American Economic Association, vol. 76(4), pages 604-620, September.
    2. A. Smith, Jeffrey & E. Todd, Petra, 2005. "Does matching overcome LaLonde's critique of nonexperimental estimators?," Journal of Econometrics, Elsevier, vol. 125(1-2), pages 305-353.
    3. James J. Heckman & Hidehiko Ichimura & Petra E. Todd, 1997. "Matching As An Econometric Evaluation Estimator: Evidence from Evaluating a Job Training Programme," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 64(4), pages 605-654.
    4. Long, Qi & Little, Roderick J. & Lin, Xihong, 2008. "Causal Inference in Hybrid Intervention Trials Involving Treatment Choice," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 474-484, June.
    5. James J. Heckman & Jeffrey A. Smith, 1995. "Assessing the Case for Social Experiments," Journal of Economic Perspectives, American Economic Association, vol. 9(2), pages 85-110, Spring.
    6. Shadish, William R. & Clark, M. H. & Steiner, Peter M., 2008. "Can Nonrandomized Experiments Yield Accurate Answers? A Randomized Experiment Comparing Random and Nonrandom Assignments," Journal of the American Statistical Association, American Statistical Association, vol. 103(484), pages 1334-1344.
    7. Burt S. Barnow & Coady Wing & M. H. Clark, 2017. "What Can We Learn From A Doubly Randomized Preference Trial?—An Instrumental Variables Perspective," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 36(2), pages 418-437, March.
    8. Thomas D. Cook & William R. Shadish & Vivian C. Wong, 2008. "Three conditions under which experiments and observational studies produce comparable causal estimates: New findings from within-study comparisons," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 27(4), pages 724-750.
    9. Dehejia, Rajeev, 2005. "Practical propensity score matching: a reply to Smith and Todd," Journal of Econometrics, Elsevier, vol. 125(1-2), pages 355-364.
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    Keywords

    breasfeeding; RCT; WIC; WSC;
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