 Solution

If the two regressors are strongly associated, the data may provide little support for varying one while holding the other fixed. Multicollinearity makes the separate slope estimates unstable, and exact linear dependence makes them unidentifiable. A conditional slope may also be an extrapolation beyond combinations actually observed.

Furthermore, a slope in a linear regression model is not automatically a causal effect. A causal interpretation needs appropriate design or assumptions excluding confounding; independence of the errors across observations and zero mean alone do not establish those assumptions. The coefficients describe the specified linear mean relation, but changing a regressor by intervention can have a different interpretation.