Plenary lecture 1: considering commonsense causal imperfection reasoning
Lawrence J. Mazlack · International Conference on Mathematical Methods and Computational Techniques in Electrical Engineering · 2011
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.