In this paper, we delve into the intricate realm of human genomics, presenting a novel design that leverages deep learning and counterfactual reasoning for causal inference. We postulate that mutations occurring within DNA sequences have the potential to instigate diseases by interrupting essential biological processes, a hypothesis that fundamentally drives this research. To test this, we have undertaken a meticulous extraction of key attributes from a range of databases hosted by the National Center for Biotechnology Information (NCBI). These attributes are subsequently processed using one-hot encoding, a technique that effectively transforms categorical variables into a form that could be provided to machine learning algorithms. A sophisticated deep learning model is then utilized to ascertain the accuracy of the hypothesis. The output, depicted as a causal graph, elucidates the relationships and interactions between the variables in question, providing a graphical representation of the proposed hypothesis. Our research suggests that strategic modifications to the DNA sequence or alterations to the set of mutations in the DNA could induce significant changes in the biological processes. This, in turn, can lead to alterations in the structure and function of proteins, a cornerstone of cellular operations. We also underline the importance of counterfactual statements in formulating hypotheses and driving intelligent behavior. Despite their untestable nature and inherent subjectivity, these counterfactuals serve as powerful tools for comprehending and predicting outcomes. The implications of this design extend beyond academic interest. It provides a pathway for a deeper understanding of human genomics and holds promise for the development of targeted therapies for genetic diseases. It fosters the possibility of personalized medicine and therapeutic strategies that can alter the course of the disease at a genetic level, potentially revolutionizing healthcare.