Re: Cause and effect

[Aiviet Nguyen <aiviet@cat.syr.edu>]

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
> 
>