Comparing statistical methods

Harold Lehmann lehmann at JHMI.EDU
Thu Aug 9 15:37:24 UTC 2018


I am on a kick these days about causal reasoning (cf Judea Pearl, The Book of Why). He points out that there are three layers of increasing abstraction: statistical, causal, and contrafactual ("what if"). (The anti-Bayesians on this list will be happy to know that Bayesian statistics is relegated to the lowest level, although suited to represent the other layers, if directed by them.)

It seems to me that decision making is all about "what if," and therefore, statistics (as traditionally conceived) are inadequate.

Harold

On Aug 9, 2018, at 9:40 AM, Elias Peter <pheski69 at GMAIL.COM<mailto:pheski69 at GMAIL.COM>> wrote:

I was directed by my son (math person, statistician, and professional data manager)to  a post in a forum where data/stats people discuss methods. It asks for information about outcome data comparing different statistical models. It might be interesting for this group to follow this. Or perhaps contribute. I have not seen any answers yet. Here is the flavor of the question:

"The overarching goal of statistics is to make decisions in the face of uncertainty.” …list of statistical approaches…  "What seems to be missing is head-to-head comparisons of approaches to see which ones optimize utility/loss/cost functions where such functions reflect real, concrete goals.” …  "Does anyone know of comparative studies that inform us of the value of two or more statistical approaches when the goal is making the best decisions?"


https://discourse.datamethods.org/t/choosing-statistical-paradigms-by-studying-the-quality-of-decisions-to-which-they-lead/372?u=f2harrell

Peter Elias



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