Re: Cause and effect

[Tuan V Nguyen <t.nguyen@garvan.unsw.edu.au>]

Hi anh AiViet and folks,

	Ah! This is the thread we have discussed quite some 
time ago. Nevertheless, your opinion is VERY interesting to 
me. I have not thought of the issue of "local" and "global" 
causality. But, I still prefer to think that you need to have 
some knowledge of the underlying phenomenon. 

	Your example of 

>    The Causality statement: If I heat the water, it will be warmer, is valid
>globally but not neccessarily locally.

is very valid. But, you have to have some background 
knowledge about the chemical reaction to make a definite 
cause-and-effect inference. Dont't you agree?

	Tuan

At 05:22 PM 4/1/97 -0600, you wrote:
>
>Hi,
>    Just a note of Causality local violation: 
>
>Globally speaking, we can say that you turn red because you are shy. That's
>OK in a long enough period of time, where you are shy at t_0 and turn red 
>at t_1 (t_0<t_1). But remember that t_1-t_0 should be greater than a critical
>amount of time for this global causality working.
>
>   If you slice the interval (t_0, t_1) into small enough pieces of time
>intervals, you will see that at the beginning the degree of being shy is
>still low and you become a litle bit red. In the next slice of time because
>you are red you become a litle bit shier. Here Cause and effect interchange.
>The Causality is violated locally....and so on until you reach t_1, you 
>will be shier than you were at t_0 and you become significantly red.
>
>    The debates usually occur because of the mismatch between global and 
>local views. The dailly life debates used to use the classical language
>like this. Statistics teaches us to recognise but ignore the local 
>observations.
>
>    The Causality statement: If I heat the water, it will be warmer, is valid
>globally but not neccessarily locally.
>
>   Cheers
>   Aiviet
>
>On Sun, 16 Mar 1997, Tuan V Nguyen wrote:
>
>> Hello anh AiViet, Huy, Vu and folks, 
>> 
>> >  I think Cause and Effect is the Raison D'E^tre of Statistics even it is 
>> >not a problem of Statistics as Anh Huy said. The most beautiful thing of 
>> >Statistics is that it changes the way we think of Causality.
>> 
>> 	Let me take a concrete example: If your wife (or lover) 
>> feels happy after you gave her a red rose, then you may say 
>> that the rose is the cause of the effect of her happiness. 
>> However, if you give her a rose, and if at that precise 
>> second, a piece of toast pops out of the eletric toaster, 
>> then it would be ludicrous to make any inference regarding 
>> the rose and the toaster. 
>> 
>> 	The distinction of cause and effect has been a subject 
>> of discussion among statisticians for quite some time. One of 
>> the main domains of statistics is the study of relationships 
>> between attributes or variables. So, when one writes the 
>> equation Y = F(X) + E, many readers immediately think that X 
>> causes Y. But, of course, the inference can never be complete 
>> without a logical reasoning of the phenomenon under study. 
>> Consider the equations
>> 
>> 	WEIGHT (in kg) = -12 + 0.5*HEIGHT (in cm),
>> 
>> and
>>  
>> 	WEIGHT (in kg) = 68 - 0.04*Age (in yrs)
>> 
>> 
>> The standard interpretation of these equations is that (1) if 
>> you can increase your height by 1 cm, you are expected to 
>> have your weight increased by 0.5 kg; and that (2) if you are 
>> celebrating your birthday tomorrow, you are expected to drop 
>> 0.04 kg in weight. But, of couse, this is only a 
>> relationship. There is no biological evidence suggesting that 
>> increase height will CAUSE increase in weight, nor is there 
>> evidence suggesting that age causes decrease in weight. What 
>> we can say is an ASSOCIATION between height and age vs. 
>> weight. In fact, a lot of relationships between phenomena can 
>> be classified as ASSOCIATION rather than CAUSATION.
>> 
>> 	A few weeks ago, there was a report that a certain drug 
>> could reduce the incidence of cholera in Vietnam. The finding 
>> was based on a study in which half of patients received the 
>> drug (treatment) and another half did not receive the drug 
>> (controls). What they actually found was that the incidence 
>> of cholera in the treatment group was significantly lower 
>> than in the control group. Based on this, can we say that the 
>> drug caused reduction in risk of cholera? Having worked with 
>> medical fellows for some time, I must say that I even doubt 
>> whether the immunization experts can answer this question 
>> properly. Statisticians have invented a wonderful word for 
>> this; they would say something along the line "the drug was 
>> ASSOCIATED with a reduction in risk of cholera".
>> 
>> 	In the last 30 yrs or so, billion of dollars have been 
>> poured into genetic research, and despite some laudable (or 
>> laughable) claims from medical researchers, we are still at 
>> dark regarding mechanisms of major genetic diseases. The 
>> inter-dependence among organs in our body is so complicated 
>> that it is thought impossible to make any inference on 
>> causation. What we can say at most is association.
>> 
>> 
>> 	Tuan V Nguyen