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Scientific investigations have two purposes: (1) discovering previously unknown associations between a natural phenomenon, and (2) generating precise mechanistic explanations for how the phenomena are causally related. Among these two, explanation is the most critical to achieve global impact; identifying the root cause of natural phenomena not only enables us to predict their future occurrences, but also implies the means in which we may prevent or treat such events (e.g., the effect of gene mutation and alteration on development of cancer). This is particularly true in the medical domain, where erroneous treatments can result in catastrophic consequences. Indeed, the medical domain mainly focuses on identifying correlations rather than causation. Such root-cause analysis and causality-based predictive modeling are critically needed for more accurate diagnosis and the timely selection of an appropriate type of therapy. This high-risk, high-reward project will focus on designing techniques by combing automated formal reasoning and artificial intelligence (AI) to discover the causal relation between events to answer deep questions on real causes of certain medical conditions. We strive for building a prominent infrastructure for collecting preliminary data and designing proof of concept techniques that demonstrate the viability of our approach based on formal reasoning and AI to extract causal structures in health and medical domains.