Successfully completed my Master's thesis focusing on the application of optimization-based causal discovery methods for time series data. I evaluated and compared three causal discovery methods (NTS-NOTEARS, DYNOTEARS, LTS-NOTEARS), finding NTS-NOTEARS to be optimal for its accuracy, robustness, and reliability. My research introduced a novel measure for analyzing model uncertainty in neural network methods and an innovative adaptation of established methods for examining delayed causation and generating Structural Vector Auto-regressive (SVAR) models. This work underscores my commitment to leveraging data science and machine learning techniques to uncover causal relationships in complex systems.Link for this work: https://ltu.diva-portal.org/smash/record.jsf?pid=diva2%3A1749596&dswid=-1030