I’ve been looking at papers involving a lot of ‘controlling for confounders’ recently and am unsure about how much weight to give their results.
Does anyone have recommendations about how to judge the robustness of these kind of studies?
Also, I was considering doing some tests of my own based on random causal graphs, testing what happens to regressions when you control for a limited subset of confounders, varying the size/depth of graph and so on. I can’t seem to find any similar papers but I don’t know the area, does anyone know of similar work?
I’ve been looking at papers involving a lot of ‘controlling for confounders’ recently and am unsure about how much weight to give their results.
Does anyone have recommendations about how to judge the robustness of these kind of studies?
Also, I was considering doing some tests of my own based on random causal graphs, testing what happens to regressions when you control for a limited subset of confounders, varying the size/depth of graph and so on. I can’t seem to find any similar papers but I don’t know the area, does anyone know of similar work?
Robust statistics is a field. Wikipedia links to http://lagrange.math.siu.edu/Olive/ol-bookp.htm which has chapters like Chapter 7-Robust Regression and Chapter 8-Robust Regression Algorithms
Thanks, I’ll give it a read.
Maybe reading Gelman’s self-contained comments on SSC’s More Confounders would make you more confused in a good way.
Cheers, glad I’m not dealing with 300 variables. Don’t think the situation is quite as dire as for sleeping pills luckily.