Popularity of technology on Stack Overflow and Hacker News: Causality Analysis
None of this is causal. For a problem to be, in a statistical sense, causally identified there must be some random or as-if random manipulation of treatment. The two major ways of thinking about causality in statistics are Judea Pearl&#x27;s DAGS (a representation of causes and effects as an acyclic graph where pathways between variables must be clear of &quot;colliders&quot; which threaten causal validity) and the Neyman-Rubin causal model (also called &quot;potential outcomes&quot;, where a unit&#x27;s outcomes under treatment and control, hypothetically, are considered).<p>One example of an identification strategy here would be to find two languages that are identical in all regards (including in their overall popularity and exposure across the world), but where one was more popular than the other on SO specifically. This would be a matched selection on observables strategy, where selection into &quot;treatment&quot; (popularity on SO) is not globally random, but random conditional on certain pre-treatment covariates, such that the non-treatment potential outcome of both languages would be expected to be the same and the difference between the observed outcomes (the level of popularity of both languages on HN) is only a product of the treatment.<p>Here&#x27;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. Now, in the world I&#x27;ve described (where the true treatment effect is zero), what statistical test involving comparing SO and HN data, even incorporating temporal ordering, would correctly come up with an estimate of 0? If your answer does not come up with an estimate of 0, then its real-world causal estimate is also presumptively wrong.<p>I also have concerns about how the author measured both treatment and outcome.<p>Overall I think there is an interesting DESCRIPTIVE (non-causal) question somewhere in this article, but it&#x27;s bogged down by the author trying to apply something they heard about from a Wikipedia article as though it were a substitute for taking causality seriously. We&#x27;ve all heard Alexander Pope&#x27;s adage that &quot;a little knowledge is a dangerous thing&quot;.