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Evaluating Treatment Protocols using Data Combination

  • Debopam Bhattacharya

In real-life, individuals are often assigned to binary treatments according to existing treatment protocols.� Such protocols, when designed with “taste-based” motives, would be productively inefficient in that the expected returns to treatment for the marginal treatment recipient would vary across covariates and be larger for discriminated groups.� This cannot be directly tested if assignment is based on more covariates than the researcher observes, because then the marginal treatment recipient is not identified.� We present (i) a partial identification approach to detecting such inefficiency which is robust to selection on unobservables and (ii) a novel way of point-identifying the necessary counterfactual distributions by combining observational datasets with experimental estimates.� These methods can also be used to (partially) infer risk-preferences which may rationalize the observed treatment allocations.� Specifically, existing healthcare datasets can be analzyed with the proposed tools to test the allocational efficiency of medical treatments.� Using our methodology on data from the Coronary Artery Surgery Study in the US, which combined experimental and observational components, we find that after controlling for age, smokers in the observational dataset had to overcome a higher threshold of expected survival relative to non-smokers in order to qualify for surgery.� Our methods are applicable when individuals cannot alter their potential treatment outcomes in response to the treatment regime, unlike in the case of law enforcement.

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File URL: http://www.economics.ox.ac.uk/materials/papers/12039/paper609.pdf
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Paper provided by University of Oxford, Department of Economics in its series Economics Series Working Papers with number 609.

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Date of creation: 01 Jun 2012
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Handle: RePEc:oxf:wpaper:609
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  1. Graham Elliott & Allan Timmermann & Ivana Komunjer, 2005. "Estimation and Testing of Forecast Rationality under Flexible Loss," Review of Economic Studies, Oxford University Press, vol. 72(4), pages 1107-1125.
  2. Charles F. Manski & John V. Pepper, 1998. "Monotone Instrumental Variables with an Application to the Returns to Schooling," NBER Technical Working Papers 0224, National Bureau of Economic Research, Inc.
  3. Kate Antonovics & Brian G. Knight, 2009. "A New Look at Racial Profiling: Evidence from the Boston Police Department," The Review of Economics and Statistics, MIT Press, vol. 91(1), pages 163-177, February.
  4. Debopam Bhattacharya & Shin Kanaya & Margaret Stevens, 2012. "Are University Admissions Academically Fair?," Economics Series Working Papers 608, University of Oxford, Department of Economics.
  5. John Knowles & Nicola Persico & Petra Todd, 1999. "Racial Bias in Motor Vehicle Searches: Theory and Evidence," NBER Working Papers 7449, National Bureau of Economic Research, Inc.
  6. Bhattacharya, Debopam, 2009. "Inferring Optimal Peer Assignment From Experimental Data," Journal of the American Statistical Association, American Statistical Association, vol. 104(486), pages 486-500.
  7. Rajeev Dehejia, 1999. "Program Evaluation as a Decision Problem," NBER Working Papers 6954, National Bureau of Economic Research, Inc.
  8. Debopam Bhattacharya & Pascaline Dupas, 2008. "Inferring Welfare Maximizing Treatment Assignment under Budget Constraints," NBER Working Papers 14447, National Bureau of Economic Research, Inc.
  9. Donald W.K. Andrews & Gustavo Soares, 2007. "Inference for Parameters Defined by Moment Inequalities Using Generalized Moment Selection," Cowles Foundation Discussion Papers 1631, Cowles Foundation for Research in Economics, Yale University.
  10. Rosen, Adam M., 2008. "Confidence sets for partially identified parameters that satisfy a finite number of moment inequalities," Journal of Econometrics, Elsevier, vol. 146(1), pages 107-117, September.
  11. Patton, Andrew J. & Timmermann, Allan, 2007. "Testing Forecast Optimality Under Unknown Loss," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 1172-1184, December.
  12. Manuela Angelucci & Giacomo De Giorgi, 2009. "Indirect Effects of an Aid Program: How Do Cash Transfers Affect Ineligibles' Consumption?," American Economic Review, American Economic Association, vol. 99(1), pages 486-508, March.
  13. James J. Heckman, 1998. "Detecting Discrimination," Journal of Economic Perspectives, American Economic Association, vol. 12(2), pages 101-116, Spring.
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