CFDP 2162R3

Counterfactuals with Latent Information


Publication Date: January 2019

Revision Date: February 2019March 2021August 2021

Pages: 59


We describe a methodology for making counterfactual predictions in settings where the information held by strategic agents is unknown. The analyst observes behavior assumed to be 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 the state 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, the counterfactual prediction is described by finitely many linear inequalities, even though the latent parameter, the information structure, is infinite dimensional.

Keywords: Counterfactuals, Bayes correlated equilibrium, information structure, linear program

JEL Classification Codes: C72, D44, D82, D83

JEL Classification Codes: C72D44D82D83

See CFDP Version(s): CFDP 2162CFDP 2162RCFDP 2162R2