IDEAS home Printed from https://ideas.repec.org/p/nbr/nberwo/29756.html
   My bibliography  Save this paper

Reinforcing RCTs with Multiple Priors while Learning about External Validity

Author

Listed:
  • Frederico Finan
  • Demian Pouzo

Abstract

This paper presents a framework for how to incorporate prior sources of information into the design of a sequential experiment. These sources can include previous experiments, expert opinions, or the experimenter's own introspection. We formalize this problem using a multi-prior Bayesian approach that maps each source to a Bayesian model. These models are aggregated according to their associated posterior probabilities. We evaluate a broad of policy rules according to three criteria: whether the experimenter learns the parameters of the payoff distributions, the probability that the experimenter chooses the wrong treatment when deciding to stop the experiment, and the average rewards. We show that our framework exhibits several nice finite sample properties, including robustness to any source that is not externally valid.

Suggested Citation

  • Frederico Finan & Demian Pouzo, 2022. "Reinforcing RCTs with Multiple Priors while Learning about External Validity," NBER Working Papers 29756, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:29756
    Note: DEV LS TWP
    as

    Download full text from publisher

    File URL: http://www.nber.org/papers/w29756.pdf
    Download Restriction: no
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Esposito Acosta,Bruno Nicola & Sautmann,Anja, 2022. "Adaptive Experiments for Policy Choice : Phone Calls for Home Reading in Kenya," Policy Research Working Paper Series 10098, The World Bank.

    More about this item

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C50 - Mathematical and Quantitative Methods - - Econometric Modeling - - - General
    • C90 - Mathematical and Quantitative Methods - - Design of Experiments - - - General
    • O12 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Microeconomic Analyses of Economic Development

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nbr:nberwo:29756. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: the person in charge (email available below). General contact details of provider: https://edirc.repec.org/data/nberrus.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.