Deep Kalman Filters
This work addresses the need for efficient learning of Kalman filters in domains like healthcare for counterfactual inference, representing an incremental advancement by applying existing variational methods to this context.
The paper tackles the problem of learning a broad spectrum of Kalman filters for time-varying phenomena, introducing a unified algorithm based on variational methods for deep generative models, and demonstrates its efficacy on the 'Healing MNIST' dataset and electronic health record data of 8,000 patients over 4.5 years for counterfactual inference.
Kalman Filters are one of the most influential models of time-varying phenomena. They admit an intuitive probabilistic interpretation, have a simple functional form, and enjoy widespread adoption in a variety of disciplines. Motivated by recent variational methods for learning deep generative models, we introduce a unified algorithm to efficiently learn a broad spectrum of Kalman filters. Of particular interest is the use of temporal generative models for counterfactual inference. We investigate the efficacy of such models for counterfactual inference, and to that end we introduce the "Healing MNIST" dataset where long-term structure, noise and actions are applied to sequences of digits. We show the efficacy of our method for modeling this dataset. We further show how our model can be used for counterfactual inference for patients, based on electronic health record data of 8,000 patients over 4.5 years.