Causality

Cause is analogical. There is not one type, flavor, or aspect of cause, but four. A formal, material, efficient, and final or teleological. Most causation concerns events which occur not separately, as in this before that, but simultaneously, where simultaneous events can be spread through time. Many causal data are embedded in time, and there two types of time series which are often confused: per se and accidental. These should not be mistaken for non-causal data series (the most common) which are all accidental. All causes are activiations of potentials by something actual. A vase is potential a pile of shards. It is made actually a pile of shards by an actual baseball. All four aspects of the cause are there: form of shards, clay fragments, efficient bat, and the pile itself as an end. Deterministic (and probability) models are epistemological; essential causal models are ontological and express true understanding of the nature of a thing. Causes, if they exist and are present, must always be operative, a proposition that has deep consequences for probability modeling. Falsifiability is rarely of interest, and almost never happens in practice. And under-determination, i.e. the possibility of causes other than those under consideration, will always be with us.