Before revision: This paper proposes a method for estimating the causal effect of a discrete intervention in observational time-series using encoder-decoder networks . Encoder-decoder networks, which are special class of recurrent neural networks (RNNs) suitable for handling variable-length sequential data, are used to predict a counterfactual time-series of treated unit outcomes using only the pre-intervention outcomes of control units as inputs.
After revision: This paper proposes a method for estimating the causal effect of a policy intervention on an outcome over time . Encoder-decoder networks, which are special class of recurrent neural networks (RNNs) suitable for handling variable-length sequential data, are used to predict a counterfactual time-series of treated unit outcomes using only the pre-intervention outcomes of control units as inputs.