Actually, this is why stats exists in the first place. Larger samples (including metastudies) are so powerful -- you can measure and predict causal impact of test factors even if you can't control for unobservables. The goal is to minimize type 1 and type 2 error. So long as those unobservables are not driving a selection bias, you get wonderful things like the central limit theorem coming to the rescue.

No one can monitor or measure everything, whether philosophically (Heisenberg uncertainty principle) or prosaically (cost). But if something is true, we can often probe it enough to get at least a low-res idea of the nature of it. This moves us light years ahead of primarily using our personal experience, gut, and vibe to establish epistemologically sound assertions.