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Understanding and misunderstanding randomized controlled trials

Author

Listed:
  • Angus Deaton

    (Princeton University)

  • Nancy Cartwright

    (Durham University and University of California San Diego)

Abstract
RCTs are valuable tools whose use is spreading in economics and in other social sciences. They are seen as desirable aids in scientific discovery and for generating evidence for policy. Yet some of the enthusiasm for RCTs appears to be based on misunderstandings: that randomization provides a fair test by equalizing everything but the treatment and so allows a precise estimate of the treatment alone; that randomization is required to solve selection problems; that lack of blinding does little to compromise inference; and that statistical inference in RCTs is straightforward, because it requires only the comparison of two means. None of these statements is true. RCTs do indeed require minimal assumptions and can operate with little prior knowledge, an advantage when persuading distrustful audiences, but a crucial disadvantage for cumulative scientific progress, where randomization adds noise and undermines precision. The lack of connection between RCTs and other scientific knowledge makes it hard to use them outside of the exact context in which they are conducted. Yet, once they are seen as part of a cumulative program, they can play a role in building general knowledge and useful predictions, provided they are combined with other methods, including conceptual and theoretical development, to discover not "what works," but why things work. Unless we are prepared to make assumptions, and to stand on what we know, making statements that will be incredible to some, all the credibility of RCTs is for naught.

Suggested Citation

  • Angus Deaton & Nancy Cartwright, 2017. "Understanding and misunderstanding randomized controlled trials," Working Papers 2017-10, Princeton University, Woodrow Wilson School of Public and International Affairs, Center for Health and Wellbeing..
    • Handle: RePEc:pri:cheawb:2017-10
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    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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