Revealing Unobservables by Deep Learning: Generative Element Extraction Networks (GEEN)

Latent variable models are crucial in scientific research, where a key variable, such as effort, ability, and belief, is unobserved in the sample but needs to be identified. This paper proposes a novel method for estimating realizations of a latent variable X^* in a random sample that contains its multiple measurements. With the key assumption that the measurements are independent conditional on X^*, we provide sufficient conditions under which realizations of X^* in the sample are locally unique in a class of deviations, which allows us to identify realizations of X^*. To the best of our knowledge, this paper is the first to provide such identification in observation. We then use the Kullback-Leibler distance between the two probability densities with and without the conditional independence as the loss function to train a Generative Element Extraction Networks (GEEN) that maps from the observed measurements to realizations of X^* in the sample. The simulation results imply that this proposed estimator works quite well and the estimated values are highly correlated with realizations of X^*. Our estimator can be applied to a large class of latent variable models and we expect it will change how people deal with latent variables.