Neha Gupta

Question about data generating process in experiments

1

Hi, For the experiment provided for the metalearners in this file https://github.com/py-why/EconML/blob/main/notebooks/Metalearners%20Examples.ipynb in section 1.1, "We use the data generating process (DGP) from [Kunzel et al.](https://arxiv.org/abs/1706.03461)" Hmm....is that true? That's...

The authors have to be separated by 'and', not commas in order for the bibtex compile...I'm pretty sure this is a broad rule and not just on my particular latex...

Citation guide?

1

Hi all, Do you have a preferred citation guide? ie authors, etc? This is an analogous guide: https://github.com/benmiroglio/pymatch

Documentation updates

2

A number of small things: - Check the pe_frac things - Add paper citation, update names/contact email - Perhaps add the algorithm diagram from the paper somewhere too - (if...

"No object to concatenate error"

2

When running with the adaptive_weights='decisionTreeCV' parameter, in late iterations, sometimes the error 'no object to concatenate' appears. It seems like users who see this error can avoid it by using...

look to see if the post_processing file for CATE and ATE and ATT is vulnerable to floating point rounding issues, and if true, how to resolve? Use a small 4...

Documentation updates needed

3

https://github.com/almost-matching-exactly/DAME-FLAME-Python-Package/blob/e37e182620ca193acf70626b393c33710102af99/dame_flame/flame_algorithm.py#L239 Looking here, it looks like "early_stop_pe_frac" should be a percent change variable from the previous predictive error, but in the documentation I didn't really describe it this way and...

I'm pretty sure that line 293 in data_cleaning.py: `if (adaptive_weights == False): # Ensure that the columns are sorted in order: binary, tertary, etc ` isn't actually needed, so maybe...

Valid input checking for unique indexes

2

We don't check this right now, and it would cause problems if someone inputs a dataframe with nonunique indexes. Add something to data_cleaning.py that checks via: `df.index.is_unique`

There isn't a concept of PE when using fixed weights if I'm correct, so this should be removed from default verbose output and specified in documentation.