Dear all, In thinking about recent lectures, I'm a bit confused. If I understood correctly, Gary mentioned one day that, really, nobody is much interested in non-causal associations. But if that's true, then I'm unclear about the implications. In a garden-variety social science journal article with some sort of regression, the authors will go through the models they report, and comment on the various independent variables that appear to have significant "effects" on the dependent variable--which sounds like they're trying to talk about a number of causal relationships simultaneously, consistent with the idea that "nobody is much interested in non-causal associations". However, the recent lectures about matching, checking for balance, research design, post-treatment bias, counterfactuals, etc. suggest that to talk about even just a *single* causal effect you need to bear down, and check and do a whole bunch of things... which, as I see it, few journal articles generally do. So what gives? Is Gary saying that existing practice is just not up to snuff--that they're being wildly unrealistic in trying to parse out several causal relationships in a single article? What then is the implication for our own best practice? Should one just pick out a single covariate on which to focus (using matching, checking balance, etc.)? Or should one go through any single regression model and, for *every* (categorical) independent variable that appears to be significant, use matching on all the other covariates? As an example, supposing you stick in religion as a covariate in a regression with countries as the unit of analysis, and while religion isn't really what you're interested in, you happen find an effect for, say, being Catholic. To talk about the suprising, apparent "effect" of religion on your outcome of interest, should you then use matching, check whether counterfactuals are inside the convex hull, etc.? Any clarification would be much appreciated. - Malcolm