In part 1 of this 2-part series, I introduced the notion of sensitivity to unmeasured confounding in the context of an observational data analysis. I argued that an estimate of an association between an observed exposure (D) and outcome (Y) is sensitive to unmeasured confounding if we can conceive of a reasonable alternative data generating process (DGP) that includes some unmeasured confounder that will generate the same observed distribution the observed data. I further argued that reasonableness can be quantified or parameterized by the two correlation coefficients (\rho_{UD}) and (\rho_{UY}), which measure the strength of the relationship of the unmeasured confounder (U) with each of the observed measures. Alternative DGPs that are characterized by high correlation coefficients can be viewed as less realistic, and the observed data could be considered less sensitive to unmeasured confounding. On the other hand, DGPs characterized by lower correlation coefficients would be considered more sensitive.

I’ve built a new Exploratory Data Analysis tool, I used it in a few presentations last year with the code on github and have now (finally) published it to PyPI.

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Tristan Handy, Founder and President of Fishtown Analytics

By Mehmet Suzen, U. of Frankfurt

Roger Peng ** 2019/01/09