but they aren't considered to be the same level of evidence as randomized controlled trials.

Quite rightly -- if we randomize, we don't care what the underlying causal structure is, we just cut all confounding out anyways. Methods (a), (b), (d) all rely on various structural assumptions that may or may not hold. However, even given those assumptions figuring out how to do causal inference from observational data is quite difficult. The problem with RCTs is expense, ethics, and statistical power (hard to enroll a ton of people in an RCT).

Epidemiology and medicine does a lot of (a), look for the keywords "g-formula", "g-estimation", "inverse probability weighting," "propensity score", "marginal structural models," "structural nested models", "covariate adjustment," "back-door criterion", etc. etc.

People talk about "controlling for other factors" when discussing associations all the time, even in non-technical press coverage. They are talking about (a).