StylesOfCauses


 * Styles of causal explanation and their relation to ideas about action**

//Theme 1:// The causes proposed reflect social actions desired/ supported because >> are places where social assumptions can influence the scientist's decision, & >> science cannot be done without decisions in these areas
 * the categories used (& thus data collected)
 * assumptions needed to make causal claims from patterns

//Theme 2 :// When people make explanations look at how they attempted to identify or locate the causation and consider alternatives/ tensions. If a scientist is emphasizing, say, a unitary cause, consider the multiple factors they are excluding. This approach will help you raise questions about the other commitments that influence their choice of questions, categories, factors, and admissable explanations. The causal explanation that is advanced often corresponds to the person's commitments to certain forms of social action, e.g. Galton (Darwin's cousin) didn't measure any environmental variables and was thus able only to reach conclusions about (supposedly) inborn characters; Davenport and others similarly denied the significance of Goldberger's experimental evidence for dietary basis of pellagra. Can you identify similar divergences in explanations of causes and proposals for action in the case of other diseases, e.g. cancer, AIDS?

// Some tensions: //

+ certain background factors |||||||| synthetic, multiple factors || ** | ** || controlled ** | ** || specified & modifiable || non- separable || generally interacting || linked in specific ways ||  || experimental || “background” || holistic || interactive, synergistic || “constructionist” ||  ||
 * local/focal, proximate, single factors <>
 * assumed
 * assumed
 * “unitary” || engineering,
 * “unitary” || engineering,

-- internal to some object (e.g. each individual person) vs. in the external relations -- in the present situation vs. in its history

causes exposed by:
 * some data |||||||| + some assumptions ||
 * naturally variable observations |||| experimentally controlled ones ||  |||| bias || plausible || bits of evidence ||   ||
 * “false” correlations |||| good comparative work ||||||||||||||  ||
 * “false” correlations |||| good comparative work ||||||||||||||  ||
 * “false” correlations |||| good comparative work ||||||||||||||  ||

//Theme 3:// More proximate causes (e.g., a lung cancer gene) are not necessarily needed to ensure the most effective action (we can encourage people to stop smoking independently of knowing the genetic mechanisms of lung cancer).