Reinforcing RCTs with Multiple Priors while Learning about External Validity / Frederico Finan, Demian Pouzo.

Format:

Book

Author/Creator:

Finan, Frederico.

Contributor:

National Bureau of Economic Research.

Pouzo, Demian.

Series:

Working Paper Series (National Bureau of Economic Research) no. w29756.

NBER working paper series no. w29756

Language:

English

Physical Description:

1 online resource: illustrations (black and white);

Place of Publication:

Cambridge, Mass. National Bureau of Economic Research 2022.

Summary:

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.

Notes:

Print version record

February 2022.