  Industrial time-series, as a structural data responds to production process
information, can be utilized to perform data-driven decision-making for
effective monitoring of industrial production process. However, there are some
challenges for time-series forecasting in industry, e.g., predicting few-shot
caused by data shortage, and decision-confusing caused by unknown treatment
policy. To cope with the problems, we propose a novel causal domain adaptation
framework, Causal Domain Adaptation (CDA) forecaster to improve the performance
on the interested domain with limited data (target). Firstly, we analyze the
causality existing along with treatments, and thus ensure the shared causality
over time. Subsequently, we propose an answer-based attention mechanism to
achieve domain-invariant representation by the shared causality in both
domains. Then, a novel domain-adaptation is built to model treatments and
outcomes jointly training on source and target domain. The main insights are
that our designed answer-based attention mechanism allows the target domain to
leverage the existed causality in source time-series even with different
treatments, and our forecaster can predict the counterfactual outcome of
industrial time-series, meaning a guidance in production process. Compared with
commonly baselines, our method on real-world and synthetic oilfield datasets
demonstrates the effectiveness in across-domain prediction and the practicality
in guiding production process
