A Novel Application of SCMs to Time Series Counterfactual Estimation in the Pharmaceutical Industry

In this paper, we present a novel application of structural causal models (SCMs) and the abduction-action-prediction procedure to a time series setting in the context of a real world problem in the pharmaceutical industry. We aim to estimate counterfactuals for the sales volume of a drug that has been impacted by the entry to the market of a competitor generic drug. We employ encoder-decoder based architectures, applying a conditional variational autoencoder and also introducing the use of conditional sparse autoencoders, which had never been used in counterfactual literature. The proposed methodology requires availability of historical event and event-less time series and has the advantage of not relying on control covariates that may be unavailable, while clearly outperforming the basic counterfactual estimate of a forecast. We evaluate our approach using our company's real-world sales dataset, as well as synthetic and semi-synthetic datasets that mimic the problem context, demonstrating its effectiveness. We have successfully applied this model in our company, providing useful information for business planning, investment allocation and objectives setting.