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 .
This paper proposes an alternative to the synthetic control method (SCM) for estimating the effect of a policy intervention on an outcome over time . Encoder-decoder recurrent neural networks (RNNs) are used to predict counterfactual time-series of treated unit outcomes using only the outcomes of control units as inputs. Unlike SCM , the proposed method does not rely on pre-intervention covariates, allows for nonconvex combinations of control units, and can handle multiple treated units. In empirical and simulated data applications, RNN-based models outperform SCM in terms of predictive accuracy while using much less information to produce counterfactual predictions .
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a method
an alternative to the synthetic control method (SCM)
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causal
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discrete intervention in observational time-series using encoder-decoder networks
policy intervention on an outcome over time
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networks, which are special class of
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suitable for handling variable-length sequential data,
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pre-intervention
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the synthetic control method
SCM
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pretreatment
pre-intervention
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Encoder-decoder networks outperform the synthetic control method in simulated and empirical data applications
In empirical and simulated data applications, RNN-based models outperform SCM in terms of predictive accuracy while using much less information to produce counterfactual predictions