Adjust for covariates in small sample size (IPTW, PS match, etc.) I have a dataset of patients with a grouping variable (groups A (control) and group B (treatment)). The two groups have sample sizes of 170 vs. 30. I would like to compare outcomes between the two treatment groups but they differ in baseline covariates. I tried propensity score matching and inverse probability treatment weights (IPTW) but both don't seem to achieve good covariate balance between the groups (as of SMD <0.1).
My guess is the sample size of the treatment group is too small. Does anybody have a recommendation how I can adjust for baseline covariates in such a small sample size?

Look to see if any of these imbalanced covariates is associated with the outcome variable through regression on the available data or via a literature review.  If there is little evidence to suggest they are associated, then you would feel that much more confident there is no harm in having the imbalance.  You may end up choosing to balance only one or two of the covariates that indeed show evidence of an association with the endpoint.  Ultimately you sill still need the "no unmeasured confounders" preface if you are going to make causal inference.  See this thread.
You could perform several sensitivity analyses to see the effect of balancing each covariate separately and in combination, sample size permitting.
Also look at this thread regarding imbalance in patient characteristics due to post-baseline events.
