Proximate and ultimate causation

A proximate cause is an event which is closest to, or immediately responsible for causing, some observed result. This exists in contrast to a higher-level ultimate cause (or distal cause) which is usually thought of as the "real" reason something occurred.

In most situations, an ultimate cause may itself be a proximate cause for a further ultimate cause. Hence we can continue the above example as follows:

Separating proximate from ultimate causation frequently leads to better understandings of the events and systems concerned.

In ordinary affairs as well as in science, engineering, and other fields, all of the characteristics of an effect will be completely explained by the set of proximate causes. If a postulated (hypothesized) set of proximate causes (also known as "direct factors") does not fully explain all of the characteristics (attributes) of the effect, then the set of direct factors is either wrong or incomplete.

The set of direct factors (of an effect) has a number of known properties:

The set of direct factors will always include:

Although the behavior in these two examples is the same, the explanations are based on different sets of factors incorporating evolutionary versus physiological factors.

These can be further divided, for example proximate causes may be given in terms of local muscle movements or in terms of developmental biology (see Tinbergen's four questions).

In analytic philosophy, notions of cause adequacy are employed in the causal mechanistic model of explanation. In order to explain the genuine cause of an effect, one would have to satisfy adequacy conditions, which include, among others, the ability to distinguish between:

1. Genuine causal relationships and accidents.

2. Causes and effects.

3. Causes and effects from a common cause.

One famous example of the importance of this is the Duhem-Quine Problem, which demonstrates that it is impossible to test a scientific hypothesis in isolation, because an empirical test of the hypothesis requires one or more background assumptions. One way to solve this issue is to employ contrastive explanations. Several philosophers of science such as Lipton argue that contrastive explanations are able to detect genuine causes. An example of a contrastive explanation is a cohort study that includes a control group, where one can determine the cause from observing two otherwise identical samples. This view also circumvents the problem of infinite regression of why's that proximate causes create.

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