Video and summary of a talk I gave at the Data Science Sydney meetup, about going beyond the what & how of predictive modelling.
Discussing the need for untested assumptions and temporality in causal inference. Mostly based on Samantha Kleinberg’s Causality, Probability, and Time.
Causality is often overlooked but is of much higher relevance to most data scientists than deep learning.
Some companies present raw data or information as “insights”. This post surveys some examples, and discusses how they can be turned into real insights.