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Www.buttiandpartners.com prof. Luciano Butti V erona – Milano www.buttiandpartners.com.

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Presentation on theme: "Www.buttiandpartners.com prof. Luciano Butti V erona – Milano www.buttiandpartners.com."— Presentation transcript:

1 prof. Luciano Butti V erona – Milano

2 CAUSALITY IN SCIENCE AND LAW The big question: when one event truly causes another? When a causal link has legal consequences? Do we need a 100% certainty about the casual link to reach a “guilty verdict”? Why?

3 The WHY question arose very early in the history of the human thought: God: “Did you eat from that tree?” Adam: “It was the woman who gave me the fruit”. Eve: “The serpent deceived me”. Note that God asked for explanation – just the facts – whereas Adam and Eve felt the need to justify: causal explanations are a man-made concept, used for passing responsibilities!

4 Natural events entered into causal explanation much later: in the ancient world, they were predetermined (caused by angry Gods and sometimes a message from them) “The Lord whose oracle is at Delphi neither reveals nor conceals, but gives a sign” (Heraclitus)

5 In summary, the agents of causal forces in the ancient world were either deities, who cause things happen for a purpose, or human beings and other animals, who possess free will (?), for which they are punished and rewarded. (by the way, what is it more effective in order to improve human behavior, punishing or rewarding?)

6 WIKI: In statistics, linear regression is an approach to modeling the relationship between a scalar dependent variable y and one or more explanatory variables denoted X. Linear regression refers to a model in which the conditional mean of y given the value of X is an affine function of X. Less commonly, linear regression could refer to a model in which the median, or some other quantile of the conditional distribution of y given X is expressed as a linear function of X. Like all forms of regression analysis, linear regression focuses on the conditional probability distribution of y given X, rather than on the joint probability distribution of y and X, which is the domain of multivariate analysis.

7 The problems began, as usual, with the engineers: namely, when machines had to be constructed to do useful jobs. In that way physical objects began acquiring causal character. A wheel turns and stops BECAUSE the wheel preceding it turned and stopped. The human operator becomes secondary.

8 Aristotle regarded explanation in terms of a PURPOSE – final cause of things. The revolution came with Galileo: a) First, HOW; Second (if possible) WHY b) Description (HOW) should be carried out in the language of Mathematics

9 David Hume: WHY is superfluous. “The nature of experience is this. We remember to have had frequent instances of the existence of one species of objects; and also remember, that the individuals of another species of objects have always attended them, and have existed in a regular order of contiguity and succession with regard to them. Thus we remember, to have seen that species of object we call flame, and to have felt that species of sensation we call heat. We likewise call to mind their constant conjunction in all past instances. Without any farther ceremony, we call the one cause and the other effect, and infer the existence of the one from that of the other.”

10 Bertrand Russel: “Causality is a relic of the old age, surviving, like monarchy, only because it is erroneously supposed to do no harm”

11 Russel was wrong: causality exists. But there are two fields where the demand for distinguishing causal from other relationship was very explicit. These fields are statistics and law. Francis Galton explained the difference between co-relation and causality What is co-relation?

12 Co-relation is the consequence of the variations of two factors being (partly) due to common causes. Everyday life examples: hats and ice-cream Legal examples: Sally Clark

13 Sally Clark was a British solicitor who was convicted of the murder of two of her sons in Clark's first son died suddenly within a few weeks of his birth in After her second son died in a similar manner, she was arrested in 1998 and tried for the murder of both sons. Her prosecution was controversial due to statistical evidence presented by pediatrician Professor Sir Roy Meadow, who testified that the chance of two children from an affluent family suffering sudden infant death syndrome was 1 in 73 million. He stated in evidence as an expert witness that "one sudden infant death in a family is a tragedy, two is suspicious and three is murder unless proven otherwise" (Meadow's law). The Royal Statistical Society later issued a public statement expressing its concern at the "misuse of statistics in the courts" and arguing that there was "no statistical basis" for Meadow's claim. She was released from prison having served more than three years of her sentence.

14 RSS on Meadow’s law: First, Meadow's calculation was based on the assumption that two SIDS deaths in the same family are independent of each other. Second, the court committed a statistical error known as the "prosecutor's fallacy".[Many press reports of the trial reported that the "1 in 73 million" figure was the probability that Clark was innocent. However, even if the "1 in 73 million" figure were valid, this should not have been interpreted as the probability of Clark's innocence. In order to calculate the probability of Clark's innocence, the jury needed to weigh up the relative likelihood of the two competing explanations for the children's deaths. Although double SIDS is very rare, double infant murder is likely to be rarer still, so the probability of Clark's innocence was quite high. Hill raises a third objection to the "1 in 73 million" figure: the probability of a child dying from SIDS is 1 in 1,300, not 1 in 8,500. Meadow arrived at the 1 in 8,500 figure by taking into account three key characteristics possessed by the Clark family, all of which make SIDS less likely. However, Meadow "conveniently ignored factors such as both the Clark babies being boys – which make cot death more likely". Moreover, the very same factors which make a family low risk for cot death also make it low risk for murder.

15 Conclusion: Physics does not need causality, but Law and Epidemiology do need it, because: a) They imply responsibility; b) Distinguishing Causality from Co-relation is mandatory especially when the targeted behavior is an omission, not an action Classical Physics, Quantum Electrodynamics (QED) and the Law

16 DISCUSSION: Do we need 100% certainty? (or do we need 100% hiding the truth)

17 DISCUSSION: Free Will, Neuroscience and the Law

18 SOURCES: PEARL, Causality Dessi’, Causa Effetto Mackie, The Cement o f the Universe Mlodinow, The Drunkard’s Walk. How Randomness Rules our Lives


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