Amit Sharma

This is related to #418. Working on a fix.

thank you for raising this issue @vicbeldo98 This may be a bug. Let me check.

Since this is the average effect on the treated, you can simply compute the average outcome (Ymean) for the treated units in your dataset. This represents the observed outcome for...

What you call `control` can also be considered as a effect_modifier. See [Vanderwheele & Robins (2007)](https://pubmed.ncbi.nlm.nih.gov/17700242/). So the regression model includes an interaction term with `control` assuming that you'd like...

@liuyxmax you can access the sklearn logisticregression model, ``` est=model.estimate_effect(estimand, method_name="backdoor.dowhy.propensity_score_matching", target_units="ate") print(est.estimator._propensity_score_model) ``` Not sure if the sklearn model provides R2, but you can obtain the coefficients by querying...

the propensity scores are already added to the dataframe. you will notice that there is a new column, `propensity_score`.

1. This is a good point. Ideally we should remove the `100000` limit and let the user specify that value. By default, it can be `None` and we let the...

This is a great direction and a high priority towards validating graphs. We have added a simple implementation of 1) by using statistical independence tests [graph_refuter.py](https://github.com/microsoft/dowhy/blob/master/dowhy/causal_refuters/graph_refuter.py) but it will be...

This might be due to a random seed issue. Can you share a small reproducible code example with your dataset? If the dataset is confidential, you can also share some...

@JasonTam this is probably a bug. For multi-dimensional treatment, there should be an option for the user to choose which treatment(s) they want to convert to placebo. This will be...