Comment: A Fruitful Resolution to Simpson's Paradox via Multiresolution Inference

Keli Liu and Xiao-Li Meng

The American Statistician, 2014, vol. 68, issue 1, 17-29

Abstract: Simpson's Paradox is really a Simple Paradox if one at all. Peeling away the paradox is as easy (or hard) as avoiding a comparison of apples and oranges, a concept requiring no mention of causality. We show how the commonly adopted notation has committed the gross-ery mistake of tagging unlike fruit with alike labels. Hence, the "fruitful" question to ask is not "Do we condition on the third variable?" but rather "Are two fruits, which appear similar, actually similar at their core?." We introduce the concept of intrinsic similarity to escape this bind. The notion of "core" depends on how deep one looks-the multi resolution inference framework provides a natural way to define intrinsic similarity at the resolution appropriate for the treatment. To harvest the fruits of this insight, we will need to estimate intrinsic similarity, which often results in an indirect conditioning on the "third variable." A ripening estimation theory shows that the standard treatment comparisons, unconditional or conditional on the third variable, are low hanging fruit but often rotten. We pose assumptions to pluck away higher-resolution (more conditional) comparisons-the multiresolution framework allows us to rigorously assess the price of these assumptions against the resulting yield. One such assessment gives us Simpson's Warning: less conditioning is most likely to lead to serious bias when Simpson's Paradox appears.

Date: 2014
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
http://hdl.handle.net/10.1080/00031305.2014.876842 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:amstat:v:68:y:2014:i:1:p:17-29

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/UTAS20

DOI: 10.1080/00031305.2014.876842

Access Statistics for this article

The American Statistician is currently edited by Eric Sampson

More articles in The American Statistician from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().