I am currently working on a problem in which we have a small dataset and are interested in the causality effect of a treatment on the outcome.
My advisor has instructed me to perform a univariate regression on each predictor with the outcome as the response, then the treatment assignment as the response. Ie, I am being asked to fit a regression with one variable at a time and make a table of the results. I asked "why should we do this?", and the answer was something to the effect of "we are interested in which predictors are associated with the treatment assignment and the outcome, as this would likely indicate a confounder". My advisor is a trained statistician, not a scientist in a different field, so I'm inclined to trust them.
This makes sense, but it's not clear how to use the result of the univariate analysis. Wouldn't making model selection choices from this result in significant bias of the estimates and narrow confidence intervals? Why should anyone do this? I'm confused and my advisor is being fairly opaque on the issue when I brought it up. Does anyone have resources on this technique?
(NB: my advisor has said we are NOT using p-values as a cut off, but that we want to consider "everything".)