3 ms·
Measuring causality is hard. Without randomized control trials, you pretty much need to assume some causal graph structure (plain data is not enough). See: "ML
by stared 8y ago
Measuring causality is hard. Without randomized control trials, you pretty much need to assume some causal graph structure (plain data is not enough).
See: "ML beyond Curve Fitting: An Intro to Causal Inference and do-Calculus" http://www.inference.vc/untitled/ http://www.inference.vc/untitled/
When it comes to this SO vs HN article based on data one can look at correlations. Even if some delayed correlations, then still - it does not imply causation (e.g. HN crowd my by faster to post links to new technologies than SO crowd to questions).
- nerdponx 8y agoSure, but you need to make your assumed graph convincing. As the GP said, Here's a simple inferential threat; the author ascribes the popularity of technology on SO as causing the popularity of a technology on HN. What if, instead, some third common cause caused both, but it caused SO spikes faster than HN spikes. You either need to account for this, or make a convincing argument that no such common cause exists.
- stared 8y agoSure, fully I agree with that.