  In this paper we propose the use of the generative AI methods in
Econometrics. Generative methods avoid the use of densities as done by MCMC.
They directrix simulate large samples of observables and unobservable
(parameters, latent variables) and then using high-dimensional deep learner to
inform a nonlinear transport map from data to parameter inferences. Our themed
apply to a wide verity or econometrics problems, including those where the
latent variables are updates in deterministic fashion. Further, paper we
illustrate our methodology in the field of causal inference and show how
generative AI provides generalization of propensity scores. Our approach can
also handle nonlinearity and heterogeneity. Finally, we conclude with the
directions for future research.
