Is propensity score matching worse than other matching methods? Gary King explained why he thought propensity score matching should not be used for matching. See Paper and the Video Lecture.
After all these years, have the academics/practitioners reached consensus on the following question:

should propensity score matching be avoided in favor of other matching methods?

Is there any counter-arguments given by other researchers to Gary King's explanation?

If you perform propensity matching and there is:

*

*a large imbalance in sample sizes between groups, you will be asked:
What objective criteria did you use during propensity matching to overcome
sample size differences?  (perhaps resampling, Monte Carlo, etc.)  Then, provide the justification for employing resampling methods.

*a notable difference in the severity of disease of patients in the
various groups, it will be difficult to find covariate patterns from
baseline medical histories (grade, stage, severity, duration of
disease) which would allow matching to be made.  This happens because
there was no randomization of subjects to different treatments
(exposure) -- as the data were likely from an administrative data set spawned from patients treated in a clinic/hospital who had widely
varying severity of disease.  Thus, there is wide variation in the treatment.  Again, patients weren't randomized to different
treatments --> low-risk patients received treatment for low-severity
disease, high-risk patients received treatment for high-severity
disease.

The above shortcomings surrounding severity are also in disagreement with the rule of thumb: "A clinical trial for a low-risk treatment will fail if high-risk patients are enrolled."
Clinicians often look to data analysts and statisticians to "salvage" the bias in their study because no randomization was performed.  Clinical trials are expensive, and the majority of clinicians only treat patients, and are not grant-funded researchers who carry out appropriately designed trials.  (only a small fraction of treated patients in an academic medical center are clinical trial members). The result is that a large "Excel" is constructed by residents/fellows (other attending clinicians) and the Excel-based data set "is the study." So what the statistician hears is: "that's a valuable data set that many people don't have," or, "it includes more than a thousand patients," or "people put a lot of effort into collection of that data." So the value that is ascribed to the data by the clinicians may be overstated.
