Counterfactuals with Latent Information

Dirk Bergemann, Benjamin Brooks and Stephen Morris

American Economic Review, 2022, vol. 112, issue 1, 343-68

Abstract: 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.

JEL-codes: D44 D82 D83 (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (2)

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Related works:
Working Paper: Counterfactuals with Latent Information (2021)
Working Paper: Counterfactuals with Latent Information (2021)
Working Paper: Counterfactuals with Latent Information (2021)
Working Paper: Counterfactuals with Latent Information (2019)
Working Paper: Counterfactuals with Latent Information (2019)
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DOI: 10.1257/aer.20210496

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