How to solve confounding issue in experimental design? In an experimental design, how to solve confounding issues? Building a regression to control for confounding variables, is it one solution?
 A: The issue you raise is a big one, and there is a huge statistical and scientific literature on experimental design, and methods for dealing with confounding variables.  I cannot do justice to this literature in a short answer, but I will try to give you some basics to get you started.  Regression analysis allows you to take account of confounding variables that are in the data by including them in the regression analysis.  You can obtain inferences about the "effects" of other variables, conditional on these would-be confounders, and this allows you to "filter them out" of your analysis, so that they do not confound your other inferences.  So yes, regression analysis is one method of dealing with confounding variables, so long as you can identify the relevant confounding variable, and obtain adequate data on it, to include it in your regression.
However, if this is the path you are inclined to take, there are several issues you will need to consider.  If you decide to try to "filter out" confounders using regression
analysis, it is important to ensure that the variables you are filtering out adequately capture the actual confounder, and this can be tricky to do.  For example, if you think "education" is a confounding variable in some analysis, you will need to decide what operational variables capture "education".  It is common to use some crude metric like "highest qualification awarded", but this does not fully (or even closely) capture the broader concept of "education".  It is therefore common in these situations for you to encounter a confounder that is difficult to measure adequately.
Another important issue with confounding in experimental design is that there may be a large number of possible confounders, and it is not always possible even to identify what these might be, let alone collect adequate data for them.  For this reason, an ideal method of dealing with confounding (in circumstances where it is feasible, ethical, etc.) is to design a randomised controlled trial (RCT) to determine the effects of a "treatment" relative to a "control".  It is also possible to use other experimental protocols such as "blinding".  Both "randomisation" and "blinding" are experimental protocols that are imposed to try to sever the statistical connection between the variable of interest in your study and any would-be confounding variables.  If used properly, these protocols can sever the statistical link between these variables, which allows you to treat statistical inferences about the treatment variable as being causal in nature.  What is especially nice (amazing!) about these methods is that they do not even require you to know what the confounding variables are in order to filter them out.
This answer should give you some basic points you need to consider.  However, I would stress that experimental design is a large and well-developed field, and it is important to familiarise yourself with the literature on this matter if you are conducting experimental design.  I recommend you start by examining experimental protocols like randomisation and blinding, to learn why these work.  This will lead you into broader discussions of causal analysis, and you can then start to learn about the interaction between statistical inferences, and inferences about underlying causal structures.  This will be a long but rewarding task.  Good luck.
