Deep generative models for time series counterfactual inference

Abstract

Decision-makers want to know how to produce desired outcomes and act accordingly, which requires a causal understanding of cause and effect. The last decades have seen an explosion in the availability of time series data and computational resources. This has spurred a large interest in data-driven algorithms able to provide counterfactual inference. ? As Richard Feynman said, "What I cannot create, I do not understand.", a classical way for counterfactual inference is to understand the underlying working mechanism of the system. Recently Deep Neural Networks have been fundamental in pushing the machine learning field forward with remarkable results in image classification, speech analysis, and machine translation. They also provide a great potential of learning data-generating mechanisms behind time series and thus support counterfactual inference. ? This thesis explores deep generative models of time series data, with a focus on time series counterfactual inference. Given a system of interest and its time-series observation, our goal is to learn the underlying data generating mechanism with deep generative models and predict potential outcomes under various circumstances. One of the most interesting developments in generative models is the introduction of models merging the powerful function approximators provided by deep neural networks with the principled probabilistic approach from graphical models. Here the Variational Autoencoder (VAE) is a seminal contribution to this emerging field. In this thesis, we develop extensions and improvements to the VAE framework to leverage more complex time series and perform effective counterfactual inference.