Is Propensity Score Matching a "MUST" for Scientific Studies? Recently, I have been reading about Propensity Score Matching :

If I have understood this correctly, Propensity Score Matching is used to construct control/treatment groups in scientific studies, in such a way that individuals in the control group are as similar as possible to individuals in the treatment group. In other words, an individual in one of these groups is "matched" to an equivalent individual in the other group. This is done to reduce the risk of "latent and unobserved" variables cofounding the effects of the treatments - and that "apples are compared to apples" instead of "apples to oranges".
At the surface, this seems to be very important - after all, if we are testing the effects of some pharmaceutical drug on two similar groups of people, we would like to avoid the risk of one these groups being comprised primarily of Olympian athletes and the other of senior citizens (assuming the goal of the study is to compare the effects of the drug on similar groups of people).
My Question: Just to clarify - do most researchers attempt to implement some form of Propensity Score Matching when conducting these kinds of statistical studies? Is this a "must"?
If some form of Propensity Score Matching is not implemented properly relative to the objective of the study, does this pose a high risk of invalidating the statistical study? According to the Wikipedia article (https://en.wikipedia.org/wiki/Propensity_score_matching), Propensity Score Matching was popularized in the 1980's - does this suggest that statistical studies conducted prior to the 1980's were more likely to suffer from these kinds of undesired variable confounding effects?

As Alexis pointed out, propensity score matching (PSM) is one of many tools we have in causal inference. Another one is Inverse Probability Weighted Estimator (IPWE). You can also use causal discovery to infer a causal diagram and use do-calculus to estimate the causal effect. Or make use of instrumental variables estimation. I'm just throwing a lot of names here (though with links, in case you want to see more about it) with a single intent: To show you there are many tools you have when your goal is causal inference in observational data. They all do something specific and have their advantages and limitations.
Should we always use such tools when we want to do causal inference in observational data? Yes. Are all scientific studies about this? No. What's the best one? It depends on what you want, how you want and what you have :-).
One last thing: Latent and unobserved are synonymous in this context. Latent confounders and unobserved confounders refer to the same thing. Besides, confounding is not your only enemy when it comes to inferring causality. Collider bias is another one and propensity score matching does not account for bias due to censoring.
