CFDP 2162R

Counterfactuals with Latent Information


Publication Date: January 2019

Revision Date: February 2019

Pages: 33


We describe a methodology for making counterfactual predictions when the information held by strategic agents is a latent parameter. The analyst observes behavior which is rationalized by a Bayesian model in which agents maximize expected utility, given partial and differential information about payoff-relevant states of the world. A counterfactual prediction is desired about behavior in another strategic setting, under the hypothesis that the distribution of and agents’ information about the state are held fixed. When the data and the desired counterfactual prediction pertain to environments with finitely many states, players, and actions, there is a finite dimensional description of the sharp counterfactual prediction, even though the latent parameter, the type space, is infinite dimensional.

Keywords: Counterfactuals, Bayes correlated equilibrium, Information structure, Type space, Linear program

JEL Classification Codes: C72, D44, D82, D83

JEL Classification Codes: C72D44D82D83

See CFDP Version(s): CFDP 2162CFDP 2162R2CFDP 2162R3