Causal Inference for Observational Time-Series with Encoder-Decoder Networks

12/10/2017 ∙ by Jason Poulos, et al. ∙ 0

This paper proposes a method for estimating the causal effect of a discrete intervention in observational time-series data using encoder-decoder recurrent neural networks (RNNs). Encoder-decoder networks, which are special class of RNNs suitable for handling variable-length sequential data, are used to predict a counterfactual time-series of treated unit outcomes. The proposed method does not rely on pretreatment covariates and encoder-decoder networks are capable of learning nonconvex combinations of control unit outcomes to construct a counterfactual. To demonstrate the proposed method, I extend a field experiment studying the effect of radio advertisements on electoral competition to observational time-series.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 11

page 16

page 17

This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.