Amit Sharma

I am using the latest version, since I installed it on my github account a few days back. I got it fixed now, here's what I did. The error was...

@cwayad are you using the latest version of dice (v0.9)? also, to reproduce your example, I need the model training code. can you provide that so that this can be...

Maybe a better name is "numeric_features", rather than `continuous_features`. Regardless, putting numeric features inside `continuous_features` fix should work for anyone facing this issue.

We have an update in v0.9 for deep learning models that allows you to specify an encoding for categorical features. `m = dice_ml.Model(model_path=dice_ml.utils.helpers.get_adult_income_modelpath(), backend='TF2', func="ohe-min-max")` Where the "ohe-min-max" is a...

This issue is fixed in v0.9.

The method from the arxiv paper is not currently implemented. Will be a great contribution if you/someone is up for it.

If you believe that you have reasonably covered all confounding variables, then the next step is to do refutations. Refutations are of two types: 1. Necessary tests like Placebo test:...

The updated link is https://py-why.github.io/dowhy/v0.8/example_notebooks/tutorial-causalinference-machinelearning-using-dowhy-econml.html

Thanks for your patience @williamty We somehow missed this. Are you still having this error? Can you share a minimum working example?

@zahs123 yes, that is the correct expression. For reference, the variables used are in this [file](https://github.com/py-why/dowhy/blob/f2a4a51a7f2b7d8f97fa6bde73baca10dfbaf0c6/dowhy/causal_estimators/propensity_score_stratification_estimator.py#L117).