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Propensity Score Matching Method in Quasi-Experimental Designs: An Approach to Program Evaluation of INHP-III

  • Kaushal Deep Gakhar
  • Vidhu Kapur
  • Navneet Kaur
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    The experimental designs are generally considered as the robust evaluation methodologies as there is random assignment. These are possible in clinical trials or in pilot phase of the project but during the development phase due to ethical issues and resource constraints; use of true experimental designs are not feasible in majority of development interventions as use of experimental design entails creation of treatment and comparison group thereby providing benefits to some and excluding others. It is unethical at program-level to provide the benefits to few and leave others and thus, there is difficulty in construction of both treatment and comparison at baseline. This makes attribution of observed outcomes and impacts to program intervention very difficult. The task gets more difficult when there are no baseline studies available. PSM offers one such alternative for addressing the concerns comparison and attribution. This paper is based on the case of Endline Evaluation of INHP- III where the Quasi-Experimental Design was employed using the PSM technique to construct the ideal comparison match for the treatment groups. [Discussion Paper 3]

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    Paper provided by eSocialSciences in its series Working Papers with number id:2782.

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    Date of creation: Aug 2010
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    Handle: RePEc:ess:wpaper:id:2782
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    1. Heckman, James J & Ichimura, Hidehiko & Todd, Petra, 1998. "Matching as an Econometric Evaluation Estimator," Review of Economic Studies, Wiley Blackwell, vol. 65(2), pages 261-94, April.
    2. Juan José Díaz & Sudhanshu Handa, 2005. "An Assessment of Propensity Score Matching as a Non Experimental Impact Estimator: Evidence from Mexico's PROGRESA Program," IDB Publications (Working Papers) 25418, Inter-American Development Bank.
    3. James Heckman & Hidehiko Ichimura & Jeffrey Smith & Petra Todd, 1998. "Characterizing Selection Bias Using Experimental Data," Econometrica, Econometric Society, vol. 66(5), pages 1017-1098, September.
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