Yes, the Occamian view is in his book in section 2.3 (and still in the 2009 2nd edition). But that definition of “inferred causation”—those arrows common to all causal models consistent with the statistical data—depends on general causal assumptions, the usual ones being the DAG, Markov, and Faithfulness properties.
“one cannot substantiate causal claims from associations alone, even at the population level—behind every causal conclusion there must lie some causal assumption that is not testable in observational studies.”
Here is a similar survey article from 2003, in which he writes that exact sentence, followed by:
“Nancy Cartwright (1989) expressed this principle as “no causes in, no causes out”, meaning that we cannot convert statistical knowledge into causal knowledge.”
Everywhere, he defines causation in terms of counterfactuals: claims about what would have happened had something been different, which, he says, cannot be expressed in terms of statistical distributions over observational data. Here is a long interview (both audio and text transcript) in which he recounts the whole course of his work.
In other places, for example: “Causal inference in statistics: An overview”, which is in effect the Cliff Notes version of his book, he writes:
“one cannot substantiate causal claims from associations alone, even at the population level—behind every causal conclusion there must lie some causal assumption that is not testable in observational studies.”
Here is a similar survey article from 2003, in which he writes that exact sentence, followed by:
“Nancy Cartwright (1989) expressed this principle as “no causes in, no causes out”, meaning that we cannot convert statistical knowledge into causal knowledge.”
Interesting, but how do those files evade word searches for the parts you’ve quoted?
Interesting, but how do those files evade word searches for the parts you’ve quoted?
Dunno, not all PDFs are searchable and not all PDF viewers fail to make a pig’s ear of searching. The quotes can be found on p.99 (the third page of the file) and pp.284-285 (6th-7th pages of the file) respectively.
Yes, the Occamian view is in his book in section 2.3 (and still in the 2009 2nd edition). But that definition of “inferred causation”—those arrows common to all causal models consistent with the statistical data—depends on general causal assumptions, the usual ones being the DAG, Markov, and Faithfulness properties.
In other places, for example: “Causal inference in statistics: An overview”, which is in effect the Cliff Notes version of his book, he writes:
“one cannot substantiate causal claims from associations alone, even at the population level—behind every causal conclusion there must lie some causal assumption that is not testable in observational studies.”
Here is a similar survey article from 2003, in which he writes that exact sentence, followed by:
“Nancy Cartwright (1989) expressed this principle as “no causes in, no causes out”, meaning that we cannot convert statistical knowledge into causal knowledge.”
Everywhere, he defines causation in terms of counterfactuals: claims about what would have happened had something been different, which, he says, cannot be expressed in terms of statistical distributions over observational data. Here is a long interview (both audio and text transcript) in which he recounts the whole course of his work.
Interesting, but how do those files evade word searches for the parts you’ve quoted?
Dunno, not all PDFs are searchable and not all PDF viewers fail to make a pig’s ear of searching. The quotes can be found on p.99 (the third page of the file) and pp.284-285 (6th-7th pages of the file) respectively.
OTOH, try Google.