Ask Why! Finding motives, causes, and purpose in data science
Video and summary of a talk I gave at the Data Science Sydney meetup, about going beyond the what & how of predictive modelling.
insights
Diving deeper into causality: Pearl, Kleinberg, Hill, and untested assumptions
Discussing the need for untested assumptions and temporality in causal inference. Mostly based on Samantha Kleinberg’s Causality, Probability, and Time.
Why you should stop worrying about deep learning and deepen your understanding of causality instead
Causality is often overlooked but is of much higher relevance to most data scientists than deep learning.
This holiday season, give me real insights
Some companies present raw data or information as “insights”. This post surveys some examples, and discusses how they can be turned into real insights.