Publication Date: January 2019
Revision Date: February 2019March 2021August 2021October 2021
We describe a methodology for making counterfactual predictions in settings where the information held by strategic agents and the distribution of payoﬀ-relevant states of the world are 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 the state. 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, D83CFDP 2162CFDP 2162RCFDP 2162R2CFDP 2162R3
See CFP: CFP 1760