What's the difference between endogenous variable and multicollinearity? Investopedia says that:

Multicollinearity is a statistical concept where independent variables
in a model are correlated.

Also Investopedia says that:

An endogenous variable is a variable in a statistical model that's
changed or determined by its relationship with other variables within
the model.

What's the difference between an endogenous variable and multicollinearity?
Does a pair of independent endogenous variables cause multicollinearity?
 A: Both definitions are awful.
Normally when you specify a model you have the left hand side (LHS) of the equation depending on its right hand side (RHS). So the purpose of modeling is to find the coefficients that will help you determine what should be LHS for a given RHS. Endogeneity arises when RHS also depends on LHS. Without getting into details, this can mess up your coefficients.
The variables on RHS not only can be, but usually are correlated with each other. It's rarely the case when they are not. That's why the definition of multicollinearity that you cited is terrible. Multicollinearity is a condition when the correlations are so high that they cause issues. This situation is not uncommon though.
A: Typically, when we talk about endogeneity, we are interested in interpreting the output from a model as causal.  Of particular concern is that there are causal variables and relationships that exist in the universe, but are not included in our model.  If so, that means that the model's estimates are biased and should not be interpreted causally (but may be fine as estimates of the marginal associations).  It is also possible that there are variables in the model that have complex causal relationships that are not captured by the way the model is set up.  For example, there may be a mediational relationship amongst the X-variables.  Another possibility is that Y causes X, and the data are being modeled the wrong way around.
Multicollinearity is when predictor variables are highly correlated with each other.  If it gets to the point where the correlation is strong enough, it becomes very difficult for the model to determine what the relationships really are.  If you were to get another dataset and fit the same model, you might find that you get very different estimates.  To account for this fact, the standard errors that the model estimates for those variables becomes very large.  A way to assess this possibility is to compute the variance inflation factors (VIFs) for your variables.  The VIF is a multiplicative factor that tells you how much larger the variance of the sampling distribution (${\rm SE}^2$) is than it would have been if the variables had been perfectly uncorrelated.  By convention, it is said that you have a problem with multicollinearity if the VIF is >10.  Considering the case where there are only 2 X-variables, that means their pairwise correlation is >.95.

*

*Note that the problem with multicollinearity is just estimating the marginal association with reasonable precision.  It isn't about whether it is appropriate to interpret that marginal association causally.  Furthermore, variables can be correlated without either being a cause of the other.


*If one variable is causally related to another, that correlation need not be very large at all to bias the interpretation of the estimate.  On the other hand, there can be a causal relation between variables without leading to a problem with collinearity, because collinearity doesn't occur until the correlation is really strong.
