It is widely understood that statistical correlation between two variables ≠ causation. But despite this admonition, people are routinely overconfident in claiming correlations to support particular causal interpretations and are surprised by the results of randomized experiments, suggesting that they are biased & systematically underestimating the prevalence of confounds/common-causation. I speculate that in realistic causal networks or DAGs, the number of possible correlations grows faster than the number of possible causal relationships. So confounds really are that common, and since people do not think in DAGs, the imbalance also explains overconfidence.
Full article: http://www.gwern.net/Causality
That is another aspect, I think, but I I'd probably consider the underlying drive to be not the desire for simplicity but the desire for the world to make sense. To support this let me point out another universal human tendency -- the yearning for stories, narratives that impose some structure on the surrounding reality (and these maps do not seek to match the territory as well as they can) and so provide the illusion of understanding and control.
In other words, humans are driven to always have some understandable map of the world around them, any map, even if not very good and even if it's pretty bad. The lack of some map, the lack of understanding (even if false) of what's happening is well-known to lead to severe stress and general unhappiness.