In the regression model $Y \sim X$, to control for the effect of confounder $Z$ (where $X \leftarrow Z \rightarrow Y$) we can use the regression model $Y \sim X + Z$, i.e., taking the average effect of $X$ on $Y$ across differnt values of $Z$.

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Given two variables X, Y and a confounder Z (i.e. X<-Z->Y). To obtain the relationship between X and Y not affected by Z, we can separately compare X and Y in every groups of Z. Then we can take an average of the effects, weighing each group according to its percentage. This method of compensation is called "adjusting for Z" or "controlling for Z" by statisticians.

Deconfounding/Adjusting/Controlling a Measurable Confounder

Regression models predict a value Y given a set of X variables.
In the regression model $Y \sim X$, we measure the effect of $X$ on $Y$, i.e., we measure the association between changes in values of $X$ and changes in values of $Y$.
Analyzing a regression model is a method used to solve a regression problem.

Regression Models

Pearl, J., & Mackenzie, D. (2018). $\mathit{The \ Book \ of \ Why: \ The \ New \ Science \ of \ Cause \ and \ Effect.}$ Basic Books.

The Book of Why

The statistical procedure of controlling for everthing that can be measured is misguided. 

For instance, in a collider $A\rightarrow B \leftarrow C$ where the path between $A$ and $C$ is initially independent, controlling for B would make them dependnet due to the explain-away effect. This means that a back-door is opened.

Also, controlling for descendents of the varibles we are interested in should also be avoided. This is like "partially" controlling for the interested variable itself. 

Controlling for everything is misguided

Deconfounding/Adjusting/Controlling a Measurable Confounder in a Regression Model

- Mean Squared Error(MSE)
- Root-Mean-Squared-Error(RMSE).
- Mean-Absolute-Error(MAE).
- R² or Coefficient of Determination.
- Adjusted R²

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