DRASTICALLY different sample sizes with categorical data I am trying to compare the rate of instance of a few variables between two datasets. Dataset A has about 3,000 observations while Dataset B has 180,000. Is it an issue to simply run Chi-Square tests with such large and different size datasets? I feel like any minute difference will result in a significant result.
I tried to use propensity scores to match observations at a 5:1 ratio, but all of the scores were either approximately 0 or 1. Any help would be appreciated.

There is nothing wrong with size disparities for the chi-square test, especially when the cell sizes are large. This is a different issue than a large overall sample size yielding statistically significant results even for small effect sizes. There is absolutely no benefit to discarding data to make your sample smaller (e.g., using propensity score matching) unless you are also reducing confounding in doing so, which you did not mention was a problem in your analysis. A statistically significant result doesn't bound you to anything or force you to do anything, so why does observing them worry you? It's up to you and your readers how to interpret such a result. If the effect is tiny then interpret it as a tiny effect. Statistical significance just means the effect is not likely to be observed simply by chance but rather reflects some structural difference between the datasets, however small that might be.
