Causal Effect Estimation With Global Probabilistic Forecasting: A Case Study of the Impact of Covid-19 Lockdowns on Energy Demand Nandipura Prasanna, Ankitha Grecov, Priscila Dieyu Weng, Angela Bergmeir, Christoph Norbert Load forecasting Uncertainty Causal effect This work was supported in part by Australian Research Council under Grant DE190100045, and in part by Monash University Graduate Research funding and MASSIVE - High Performance Computing Facility, Australia. Paper no. TPWRS-01592-2022. This article has supplementary material provided by the authors and color versions of one or more figures available at https://doi.org/10.1109/TPWRS.2023.3296870 The electricity industry is heavily implementing intelligent control systems to improve reliability, availability, security, and efficiency. This implementation needs technological advancements, the development of standards and regulations, as well as testing and planning.Load forecasting and management are critical for reducing demand volatility and improving the market mechanism that connects generators, distributors, and retailers. During policy implementations or external interventions, it is necessary to analyse the uncertainty of their impact on the electricity demand to enable a more accurate response of the system to fluctuating demand. This article analyses the uncertainties of external intervention impacts on electricity demand. It implements a framework that combines probabilistic and global forecasting models using a deep learning approach to estimate the causal impact distribution of an intervention. The causal effect is assessed by predicting the counterfactual distribution outcome for the affected instances and then contrasting it to the real outcomes.We consider the impact of Covid-19 lockdowns on energy usage as a case study to evaluate the non-uniform effect of this intervention on the electricity demand distribution. We could show that during the initial lockdowns in Australia and some European countries, there was often a more significant decrease in the troughs than in the peaks,while themean remained almost unaffected. 2024-07-18T11:48:25Z 2024-07-18T11:48:25Z 2024-03-02 journal article Nandipura Prasanna, A. IEEE transactions on power systems, vol. 39, no. 2, march 2024. [ https://doi.org/10.48550/arXiv.2209.08885] https://hdl.handle.net/10481/93230 10.48550/arXiv.2209.08885 eng http://creativecommons.org/licenses/by/4.0/ open access Atribución 4.0 Internacional IEEE