>you can never infer causality from looking passively at data

In this view, causal inference is relegated only to a single observation. Extrapolating results to any other similar expiremental set-up (even identical) is just that. To quote Hume,

>I say, then, that, even after we have experience of the operation of cause and effect, our conclusions from that experience are not founded on any reasoning, or any process of the understanding

There is an epistemological limit of the concept of causation. In statistical inference, based on probability theory, a good professor will use this limit to routinely used to smack undergrads upside the head- be it regression or p-values.

We assume distributions of underlying samples, along with central limit theorem to do statistics that support causal inference. We can attempt to control for type 1 error via our set-up, but even when our assumptions are not violated we still can never claim a result with 100% certainty.

Carefully controlled experimentation is better than using some observation set, but it suffers two drawbacks- it is expensive to obtain and it's uses beyond the experiment are quite limited, necessarily requiring extrapolation. So I argue pragmatically that we should use latent data and the statistical tools at our disposal to understand causation (to the extent it actually exists) with the appropriate limiting caveats.