Currently we only get point estimates of the causal effects, assuming that our causal graph is flawless. If the graph is wrong, we have no idea how bad our estimation results can get.
It would lend additional credibility to our analyses if we could specify multiple possible graphs (e.g. because we are not sure about the presence of one edge), estimate the causal effects based on each of the graphs and return something like a confidence interval instead of the current point estimates. A visual representation could be added to the automated pdf report.
I already have implemented a prototypical solution, just need to refactor and integrate it properly.
Currently we only get point estimates of the causal effects, assuming that our causal graph is flawless. If the graph is wrong, we have no idea how bad our estimation results can get.
It would lend additional credibility to our analyses if we could specify multiple possible graphs (e.g. because we are not sure about the presence of one edge), estimate the causal effects based on each of the graphs and return something like a confidence interval instead of the current point estimates. A visual representation could be added to the automated pdf report.
I already have implemented a prototypical solution, just need to refactor and integrate it properly.