Is the physical impact / effect necessarily the independent variable / dependent variable of the regression model A regression analysis (RA) is often explained as follows:
"...Regressions analyses are statistical methods, by which you can calculate, whether an independent variable (IV) impacts a dependent variable (DV). So, in contrary to a correlation it is possible to evaluate causal effects and not only relations. Within RAs the IV is also called predictor, cause or regressor, whereas the DV is often called target variable, effect or regressand..."
What now is confusing me, is the equating of the terms independent variable and cause, or of the terms dependent variable and effect, respectively, - when reflecting what is the calculation direction of my regression model and what is the natural or physical impact-effect direction.
Lets take an example:
At a certain date we measure the water depth at several points (at the inclined ground) of a large, clear lake. For these points at the same date we also have the reflection values from satellite-borne imagary. Say we are interested in the reflection of the blue band. (Normally, for clear water we would assume a negativ correlation, i.e. blue reflection decreases while depth is increasing.)
Now we want to know whether we can predict depth by the blue band reflection values.
Now, in a regression model depth is the DV, and reflection is the IV, as the input is the reflection and the output is depth. Thus, if equalizing the terms IV and cause (or vice versa DV and effect), reflections values would impact water depth. But from a physical impact-effect point of view it is the complete opposite: water depth causes the measured reflection value (but reflection does not cause depth).
May anybody resolve this contradiction or help me with a link to an according post?
Or in other words: Is it allowed to use a regression like in the example in which the calculation direction goes the opposite of the 'physical' impact-effect direction?
Might be a quite basic question. However, thanks in advance

Independent and dependent relate to controlled experiments in which a particular variable is controlled by the experimenter who can set that particular variable. This is considered an independent variable because it is determined by the experimenter and it is not dependent other variables. Those other variables are called dependent variables because their distribution is potentially dependent on the value of the controlled independent variable.

Now, in a regression model depth is the DV, and reflection is the IV, as the input is the reflection and the output is depth.

We should only speak of dependent and independent variables in the special case that we have an experiment where one set of variables is controlled (independent).

A regression analysis (RA) is often explained as follows: "...Regressions analyses are statistical methods, by which you can calculate, whether an independent variable (IV) impacts a dependent variable (DV). So, in contrary to a correlation it is possible to evaluate causal effects and not only relations. Within RAs the IV is also called predictor, cause or regressor, whereas the DV is often called target variable, effect or regressand..."

This is not a correct explanation. Or, it is only correct in a specific sense.
