You may have modeled (or asked your data science team to model) the same data in R and Python. Why? Most data science teams use both R and Python, with team members specializing in one or the other. So, this could be a model changing hands. Or maybe you wanted to make sure the package implementation behaved as intended. You may also have needed better computational efficiency (R fixest can be much faster than Python linearmodels on panel data).
For whatever reason, when you run models in R and Python, you may have run into the following situation: The parameter estimates are the same, but the standard errors (and p-values) are different. The data and the model are exactly the same. So you can’t explain why, and you don’t know which standard error / statistical significance test to trust and report to the business.
If you’re curious about the most common reason, check out another previously missing section now published in the Causal Book, here. We now discuss this as part of our exercise on applying the same instrumental variable model in R vs. Python.