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 .
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. Unlike the synthetic control method, the proposed method does not rely on pretreatment covariates , allows for nonconvex combinations of control units, and can handle multiple treated units. Encoder-decoder networks outperform the synthetic control method in simulated and empirical data applications .
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using only the pre-intervention outcomes of control units as inputs. Unlike the synthetic control method, the
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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
units, and can handle multiple treated units. Encoder-decoder networks outperform the synthetic control method in simulated and empirical data applications