Abstract: A commonsense understanding of causal relationships is the key element of day-to-day decision making. Generally, commonsense causal relationships are drawn from non-experimental or observational data. Commonsense causal understanding accepts that causal relationships can be formed on incomplete or imprecise data. A complete definition of causality may not be possible or recognizing any particular causal relationship may be either obscure or overly complex. Commonsense causality is necessarily imprecise. Causal relationships may be in complexes that can be simplified at the price of increasing imprecision.
Commonsense understanding of the world tells us that we have to deal with imprecision, uncertainty and imperfect knowledge. This is also the case with scientific knowledge of the world. A difficulty is striking a good balance between precise formalism and commonsense imprecise reality.
Perhaps, complete knowledge of all possible factors might lead to a crisp understanding of whether an effect will occur. However, it is unlikely that all possible factors can be known for most situations, as the knowledge of at least some causal effects is imprecise for both positive and negative descriptions. Consequently, causal reasoning must accommodate inherent ambiguity and imprecision.

Brief Biography of the Speaker:
Professor Mazlack studied computer science and applied mathematics at Washington University (St. Louis) and electrical engineering at both SDSM&T and Marquette University. He received his Doctorate of Science from Washington University. He also studied philosophy at both Washington University and at Marquette University. Along the way to his degrees, he did research in computer science, electrical engineering, and biomedical engineering. At Marquette both a Bacon Scholarship and an athletic scholarship (football) supported him. He is a member of the Omega Rho honorary. He has been a visiting scholar at the University of California, Berkeley (imprecise reasoning) and at the University of Geneva (computational linguistics). He is on the editorial board of several journals and has served on the program committee of many conferences. Dr. Mazlack currently is at the University of Cincinnati where he is the head of the Applied Artificial Intelligence Laboratory and the chair of the Data and Knowledge Management research group. Beyond academia, at a large computer company, he was responsible for database software development. He has been closely involved with several small company startups. Away from technology, he has been professionally active in the visual, written, and dramatic arts. Dr. Mazlack's current research is directed toward three areas:
•   Causality, both theoretical and applied to observational data.
• Unsupervised data mining and the closely associated topic of autonomous recognition of web page ontologies in the context of the Semantic Web.
•   Clustering multi-modal computational objects. These interests are in the context of broader interests in: soft computing, natural language understanding, artificial intelligence, and databases.