As for Econometrics, I agree that studies employing observational causal inference in statistics are much harder (and harder to swallow) than experiments. Usually.
But perhaps, if you dig a bit deeper and ask yourself things like: "what makes a hypothesis falsifyable" or, "could there be a hypothesis falsifyable by observational data?". Have your ever written down the exact exclusion condition (or exogeneity condition or whatever) you use in your experiment? You might find out something interesting.
Controlled experiments, much like any econometric (or statistic) technique, require identification assumptions. And of course, these can not be proven. Even though many people believe it is so, experiments are not really different in kind, the assumptions are just usually easier to believe. Not always, though, which can also be fun. Even in an experiment, you - in fact - observe reality only once. In other cases, however, we find out that our assumptions about the identification in the experiment were wrong, due to factors we didn't know about. This happens even in physics (shocker, I know).
Keep an open mind, would be my advise. Your critiques are well known, and in the broad sense tackling them is the business of statistics. By the way, there are indeed smart people here and there.
A good heuristic: If your find yourself rejecting entire disciplines, like econometrics, all while implictily using auction theory, regulation, pricing etc. in your amazon, ebay, cellphone, or energy grid, you are probably on the wrong peak of a Dunning Kruger curve.