Randomized Controlled Experiments

  • Jean-Michel Josselin
  • Benoît Le Maux


In many cases, the presence of confounding factors makes the identification of causal effects rather difficult. One solution to avoid potential bias is to run a randomized controlled experiment, either in the form of a clinical trial or a field experiment (Sect. 13.1). The basic tenet is to assign the subjects to a control group and a treatment group, such that they share similar characteristics on average (Sect. 13.2). The impact of an intervention is then obtained by comparing the average outcomes observed in both groups and testing whether the difference is significant (Sect. 13.3). An important issue is to assess the risks of type I and type II errors, i.e. the probabilities that the statistical test yields the wrong conclusion (Sect. 13.4). Controlling for those risks implies finding the minimum number of subjects to enroll in the experiment to achieve a given statistical power (Sect. 13.5). Another issue is to select an indicator (e.g., absolute risk reduction, relative risk ratio, odds ratio, number needed to treat) in order to point out the number of successes and failures in each group (Sect. 13.6). The analysis can also be extended to a more general framework were the timing of event occurrence is explicitly accounted for, via the estimation of survival curves with the Kaplan-Meier approach (Sect. 13.7) and the implementation of the Mantel-Haenszel test (Sect. 13.8).


Treatment Control Randomization Statistical power Survival curves Mantel-Haenszel test 


  1. Attanasio, O., Meghir, C., & Santiago, A. (2012). Education choices in Mexico: Using a structural model and a randomized experiment to evaluate PROGRESA. Review of Economic Studies, 79, 37–66.CrossRefGoogle Scholar
  2. Cochran, W. (1954). Some methods for strengthening the common chi-square tests. Biometrics, 10, 417–451.CrossRefGoogle Scholar
  3. Crépon, B., Duflo, E., Gurgand, M., Rathelot, R., & Zamora, P. (2014). Do labor market policies have displacement effects? Evidence from a clustered randomized experiment. Quarterly Journal of Economics, 128, 531–580.CrossRefGoogle Scholar
  4. Cutler, S., & Ederer, F. (1958). Maximum utilization of the lifetable method in analyzing survival. Journal of Chronic Diseases, 8, 699–712.CrossRefGoogle Scholar
  5. Deaton, A. (2010). Instruments, randomization, and learning about development. Journal of Economic Literature, 48, 424–455.CrossRefGoogle Scholar
  6. Deaton, A., & Cartwright, N. (2016). Understanding and misunderstanding randomized control trials. National Bureau of Economic Research w22595.Google Scholar
  7. Duflo, E., Glennester, R., & Kremer, M. (2007). Using randomization in development economics research: A toolkit. In P. Schultz & J. Strauss (Eds.), Handbook of development economics (vol. 4, pp. 3895–3962).
  8. Duflo, E., Dupas, P., & Kremer, M. (2015). Education, HIV, and early fertility: Experimental evidence from Kenya. American Economic Review, 105, 2757–2797.CrossRefGoogle Scholar
  9. Favereau, J. (2016). On the Analogy between Field Experiments in Economics and Clinical Trials in Medicine. Journal of Economic Methodology, 23, 203–222.CrossRefGoogle Scholar
  10. Freedman, B. (1987). Equipoise and the ethics of clinical research. New England Journal of Medicine, 317, 141–145.CrossRefGoogle Scholar
  11. Friedman, L., Furberg, C., & DeMets, D. (2010). Fundamentals of clinical trials. Heidelberg: Springer.CrossRefGoogle Scholar
  12. Kaplan, E., & Meier, P. (1958). Non parametric estimation from incomplete observations. Journal of the American Statistical Association, 53, 457–481.CrossRefGoogle Scholar
  13. Mantel, N., & Haenszel, W. (1969). Statistical aspects of the analysis of data from retrospective studies of disease. Journal of the National Cancer Institute, 22, 719–748.Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Jean-Michel Josselin
    • 1
  • Benoît Le Maux
    • 1
  1. 1.Faculty of EconomicsUniversity of Rennes 1RennesFrance

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