Job scheduling is the process where jobs are arranged in a specific sequence. This process has always been a crucial subject for companies in numerous industries. A company could significantly improve its business performance if the scheduling process is not performed optimally. As companies and products become more and more data-driven, new tools to improve processes are emerging. Robust job scheduling methods optimize job schedules with job duration uncertainties. Typical robust scheduling methods only use straightforward statistical metrics such as the job duration and variance. Data relevant to the process' job duration, stored as observational data, is not typically used in robust job scheduling methods. A field of research that is concerned with the use of observational data is causal inference. It can be applied to observational data to explain causal variable behaviour and therefore also the statistical metrics used in the scheduling process. In addition, it can be used to predict variable behaviour after certain variable modifications. This motivates our search for an approach that uses the tools of causal inference on available process data to identify the causal relations within these job processes. Based on this information, it predicts the effects of parameter modifications. These predictions are incorporated in the scheduling optimization to potentially further improve the job scheduling performance. In this thesis, we will therefore study research on causal inference and a method of robust job scheduling and propose an algorithm that extends the robust job scheduling method. The algorithm includes the tools of causal inference and additionally solution sensitivity analysis.