Showing posts from December, 2021

Uncertainties: Statistical, Representational, Interventional

 by AMIR NAJMI & MUKUND SUNDARARAJAN Data science is about decision making under uncertainty. Some of that uncertainty is the result of statistical inference, i.e., using a finite sample of observations for estimation. But there are other kinds of uncertainty, at least as important, that are not statistical in nature. This blog post introduces the notions of representational uncertainty and interventional uncertainty to paint a fuller picture of what the practicing data scientist is up against. Data science and uncertainty Data Science (DS) deals with data-driven decision making under uncertainty . The decisions themselves may range from "how much data center capacity should we build for two years hence?" or "does this product change benefit users?" to the very granular "what content should we recommend to this user at this moment?" This kind of decision making must address particular kinds of uncertainty. Wrestling with uncertainty characterizes the