@f2harrell’s propensity score question re-surfaced this broader question that has come up a few times for me in the last month or so, as I am starting work with a group that does a lot of propensity score matching:
Do you generally prefer PS, or Penalized regression (whether Bayes or frequentist) for causal modeling from non-randomized studies? If so, why?
The best arguments I have heard in favour of PS methods have been focused on simplicity of interpretation/data reduction.
- You can create a complex model for your propensity score but clinicians interpreting your actual treatment effect are presented with much more familiar methods that they are used to from RCTs.
- The popularity of these methods also seems to make more complex issues (time varying covariates) seemingly more accessible as well.
The best arguments I’ve heard for regression methods are:
- Fully conditional which may be beneficial depending on how you use the results (I usually go this way so I can model interactions explicitly, but maybe I’m off base?)
- If you’re working within a Bayesian framework, weighting observations through the likelihood is not technically correct since it’s not really a generative model.
There also seems to be a real lack of tutorials for non-statisticians laying out for example: a complex PS model and it’s regression equivalent in terms of code. I may just not be great at finding these though.