Causality, Models, Reasoning and Inference

Discussion in 'Tài Liệu Học Tiếng Anh' started by thinganbui, Apr 25, 2017.

  1. thinganbui

    thinganbui Guest

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    1.1 Introduction to probability theory
    1.1.1 Why Probabilities? Causality connotes lawlike necessity, whereas probabilities connote exceptionality, doubt, and lack of regularity. Still, there are two compelling reasons for starting with, and in fact stressing, probabilistic analysis of causality; one is fairly straightforward, the other more subtle. The simple reason rests on the observation that causal utterances are often used in situations that are plagued with uncertainty. We say, for example, “reckless driving causes accidents” or “you will fail the course because of your laziness” (Suppes 1970), knowing quite well that the antecedents merely tend to make the consequences more likely, not absolutely certain. Any theory of causality that aims at accommodating such utterances must therefore be cast in a language that distinguishes various shades of likelihood – namely, the language of probabilities. Connected with this observation, we note that probability theory is currently the official mathematical language of most disciplines that use causal modeling, including economics, epidemiology, sociology, and psychology. In these disciplines, investigators are concerned not merely with the presence or absence of causal connections but also with the relative strengths of those connections and with ways of inferring those connections from noisy observations. Probability theory, aided by methods of statistical analysis, provides both the principles and the means of coping with – and drawing inferences from – such observations.
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