"...we do not learn these inferences "just by observing the world", we learn these inferences by acting on the world and observing the results..."
The article: "“Machine learning often disregards information that animals use heavily: interventions in the world, domain shifts, temporal structure — by and large, we consider these factors a nuisance and try to engineer them away,” write the authors of the causal representation learning paper. “In accordance with this, the majority of current successes of machine learning boil down to large scale pattern recognition on suitably collected independent and identically distributed (i.i.d.) data.”"
The key words are "interventions in the world." The article goes on to say, "“Generalizing well outside the i.i.d. setting requires learning not mere statistical associations between variables, but an underlying causal model,” the AI researchers write." The point being that, whether or not acting in the world is an essential condition for learning causality, current machine learning approaches are not even trying for causality.
The article: "“Machine learning often disregards information that animals use heavily: interventions in the world, domain shifts, temporal structure — by and large, we consider these factors a nuisance and try to engineer them away,” write the authors of the causal representation learning paper. “In accordance with this, the majority of current successes of machine learning boil down to large scale pattern recognition on suitably collected independent and identically distributed (i.i.d.) data.”"
The key words are "interventions in the world." The article goes on to say, "“Generalizing well outside the i.i.d. setting requires learning not mere statistical associations between variables, but an underlying causal model,” the AI researchers write." The point being that, whether or not acting in the world is an essential condition for learning causality, current machine learning approaches are not even trying for causality.