Can I use a continuous variable as a confounder? I am validating causality for a model that looks like below. Risk and age are continuous variables, intervention is binary. Age is numeric with intervals of a year, it is also a confounder. I was planning to use all the variables as such, but I read that continuous variables cannot be used as confounders directly in a regression model without some esoteric encoding. How can I use age without converting it to binary? How can I approach this problem?
$$
Risk = a+b\cdot Intervention +c\cdot Age + e
$$


continuous variables cannot be used as confounders directly in a regression model without some esoteric encoding

This is false. You can include age into your model as is. If you believe the effect of age on risk is nonlinear, you can use an appropriate transformation of age instead.
(Encoding is needed for categorical variables, not continuous ones, and is done by using dummy variables.)
