semvec/pypi-packages huggingface dataset:
causal-attribution 1.1
Select and score features for causal inferences.
Requires Python: >=3.6
Keywords: feature selection


This package has two interfaces:

(1) score_vocab(): Given text (T), vocab (V), outcome(s) Y, and 
    confound(s) (C), this method will score each element of the
    vocab according to how well it explains each Y, controlling 
    for all of the C's.

(2) evaluate_vocab(): Measure's the strength of a vocab's causal
    effects on Y (controlling for C).

(c) Reid Pryzant 2019 https://cs.stanford.edu/~rpryzant/
May be used and distributed under the MIT license.
missing-mnv 0.0.1
Missing Values Handler
Requires Python: >=3.6

There may be some missing values in your dataset which are very important to be dealt with. This package helps in handling the missing values in your dataset. It replaces the missing values with the mean of that column. It also prints the dataset after imputing the missing values.

How to make it work :
Command for running the package will look like - 
python missing_mnv <inputdata>
e.g. python missing_mnv "mydata.csv"