Interested parties are welcome to attend.
Presentation Title: "Revealing Unobservables by Deep Learning"
Abstract:
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.
Contact Jeroen Dalderop for information.