Subject: BHA: RE: Predictions
Date: Thu, 25 Mar 2004 11:41:44 -0000
From: "Bailey,DJ  (pgr)" <D.J.Bailey-AT-lse.ac.uk>


by coincidence, I was looking at the reading list for a course taught by Adam Przeworski, in which covers this topic.
 
he also references the article suggested by Hans - and a couple of others.
 
See week 3 in http://www.nyu.edu/gsas/dept/politics/grad/syllabi/G53.3400_przeworski_f03.pdf

	-----Original Message----- 
	From: Hans G. Ehrbar [mailto:ehrbar-AT-lists.econ.utah.edu] 
	Sent: Thu 25/03/2004 11:36 
	To: bhaskar-AT-lists.village.virginia.edu 
	Cc: bhaskar-AT-lists.village.virginia.edu 
	Subject: BHA: Predictions
	
	


	Ann,
	
	since you are used the word "counterfactual" you may be interested
	in the following reference:
	
	-AT-Article{Holland1986SCI,
	  author =       "Holland, Paul W.",
	  title =        "Statistics and Causal Inference",
	  journal =      "JASA",
	  year =         1986,
	  volume =       81,
	  number =       396,
	  pages =        "945--960",
	}
	
	The authors do not know anything about CR but I think their
	approach is very compatible with depth realism.  I wrote a
	brief summary in chapter 17 of my econometrics class notes
	
	http://www.econ.utah.edu/ehrbar/ecmet.pdf
	
	
	If you download
	
	http://www.econ.utah.edu/ehrbar/ecmet.zip
	
	you will find additional literature references and comments
	in the file causali.tex, which is the Latex source code for
	this chapter.  Here is a brief excerpt from this file:
	
	
	 Rubin introduces ``counterfactual''
	 (or, as Bhaskar would say, ``transfactual'') elements
	 since he is not only talking about the value
	 a variable takes for a given individual, but also the
	 value this variable would have taken for the same individual
	 if the causing variables (which Rubin also calls ``treatments'')
	 had been different.
	 For simplicity, Holland assumes here
	 that the treatment variable has only two levels:
	 either the individual receives the treatment,
	 or he/she does not (in which case he/she belongs to the ``control'' group).
	 The correlational view would
	 simply measure the average response of those individuals
	 who receive the treatment,
	 and of those who don't.
	 Rubin recognizes in his model
	 that the same individual may or may not
	 be subject to the treatment,
	 therefore the response variable has
	 two values, one being the individual's response
	 if he or she receives the treatment,
	 the other the response if he or she does not.
	
	 A third variable indicates who receives the treatment.
	 I.e, he has the ``causal indicator'' $\rascal{s}$
	 which can take two values, $t$ (treatment)
	 and $c$ (control),
	 and two variables $\elem\yn_t$ and $\elem\yn_c$,
	 which, evaluated at individual $\vlue\selm$,
	 indicate the responses this individual would give
	 in case he was subject to the treatment,
	 and in case he was or not.
	
	 Rubin defines $\elem\yn_t-\elem\yn_c$ to be the causal effect of
	 treatment $t$ versus the control $c$.
	 But this causal effect cannot be observed.
	 We cannot observe how those indiviuals
	 who received the treatement would have responded
	 if they had not received the treatment,
	 despite the fact that this non-actualized response is just as
	 real as the response which they indeed gave.
	 This is what Holland calls
	 the \emph{Fundamental Problem of Causal Inference}.
	
	
	--
	Hans G. Ehrbar   http://www.econ.utah.edu/ehrbar   ehrbar-AT-econ.utah.edu
	Economics Department, University of Utah     (801) 581 7797 (my office)
	1645 Campus Center Dr., Rm 308               (801) 581 7481 (econ office)
	Salt Lake City    UT 84112-9300              (801) 585 5649 (FAX)
	
	
	
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