Why machine learning struggles with causality
The article makes a big unfounded assertion in one of the first few sentences &quot;We learn them at a very early age, without being explicitly instructed by anyone and just by observing the world.&quot; - we do <i>not</i> learn these inferences &quot;just by observing the world&quot;, we learn these inferences by <i>acting</i> on the world and observing the results, and arguably they are simply not learnable by pure observation alone. There&#x27;s some theoretical grounding for that (IIRC Judea Pearl&#x27;s book Causality might be a good source for that) and some experimental grounding for that (e.g. the two kitten experiment for which I can&#x27;t quickly find the actual paper but it&#x27;s described at <a href="https:&#x2F;&#x2F;io9.gizmodo.com&#x2F;the-seriously-creepy-two-kitten-experiment-1442107174" rel="nofollow">https:&#x2F;&#x2F;io9.gizmodo.com&#x2F;the-seriously-creepy-two-kitten-expe...</a> ), also other fields like medicine have a good understanding of limitations of what knowledge can be gained from purely observational studies versus experimental interventions. So it seems a bit misleading to write a whole article about &quot;why machine learning struggles with causality&quot; without even addressing the key difference of learning from observations vs learning from interaction, which IMHO is a much more fundamental obstacle than everything the article menstions.