000			01316nam a22001457a 4500
999			_c520319 _d520319
008			220907b ||||| |||| 00| 0 eng d
100			_aBergemann, D. Brooks, B. and Morris, S. _933912
245			_aCounterfactuals with latent information
260			_aAmerican Economic Review
300			_a112(1), Jan, 2022: p.343-368
520			_aWe 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. – Reproduced
773			_aAmerican Economic Review
906			_aPROJECT MANAGEMENT
942			_cAR