Philisophical Phun - Causality

In everyday life, as well as in science, we have to deal with and act on the basis of partial (i.e. incomplete, uncertain, or even inconsistent) information. Causality considers causal claims and the scientific process by which they are established, particularly where it involves the use of statistical evidence. The two fundamental questions of causality are: (1) What empirical evidence is required for legitimate inference of cause-effect relationships? (2) Given that we are willing to accept causal information about a phenomenon, what inferences can we draw from such information, and how?
“Probabilistic Causation” designates a group of theories that aim to characterize the relationship between cause and effect using the tools of probability theory. The central idea behind these theories is that causes change the probabilities of their effects. The central idea behind probabilistic theories of causation is that causes change the probability of their effects; an effect may still occur in the absence of a cause or fail to occur in its presence. Thus smoking is a cause of lung cancer, not because all smokers develop lung cancer, but because smokers are more likely to develop lung cancer than non-smokers. This is entirely consistent with there being some smokers who avoid lung cancer, and some non-smokers who succumb.
It has long been held that causation can only be reliably inferred when we can intervene in a system so as to control for possible confounding factors. For example, in medicine, it is commonplace that the reliability of a treatment can only be established through randomized clinical trials. Starting in the early 1980's, however, a number of techniques have been developed for representing systems of causal relationships, and for inferring causal relationships from purely observational data. The name ‘causal modeling’ is often used to describe the new interdisciplinary field devoted to the study of methods of causal inference. This field includes contributions from statistics, artificial intelligence, philosophy, econometrics, epidemiology, psychology, and other disciplines.
The influence of artificial intelligence and the availability of powerful computer languages holds promise that intuition can be expressed, not suppressed. I believe that as the process matures, we will see the time frame from concept to implementation shrink considerably. We have seen how 3-D computer modeling has revolutionalized the component manufacturing process. I think that advances in health care and the pharmaceutical industries will be similarily revolutionalized by this type of process.