Hi @matekadlicsko. Sorry for the slow reply, some crazy things happened in my life!

Thanks for the work on this!

Once things are more progressed, would you be good to:

• add some relevant entries in the glossary (in docs/glossary.md)?
• add one (or more) example notebooks for the docs to demonstrate use?

Hi @drbenvincent , I hope everything's fine. Sure, I'll add a few tests as well as soon as I'm ready with implementing all the basic functionalities I envisioned.

This PR has prompted me to ramp up on meta learners, it's not something I knew about before. There is a really excellent summary of them in this video by @ShawhinT.

We should definitely include a link to that material when it comes to completing the glossary or example notebooks👍

I think this has to do with the lack of doc-strings. Try commenting https://github.com/pymc-labs/CausalPy/blob/main/.pre-commit-config.yaml#L36-L42 and then run it again. You can also run git commit -m"my message" -n to ignore the pre-commit and push it to test against the ci/cd in the github actions.

I don't think I'll be able to work on this a lot this week unfortunately. I'll share where it lacks and what my plans are for (hopefully) the next week.

• Uplift, QINI and cumulative gain diagrams will be implemented, MetaLearner.plot should be rethought using these.
• In the PyMC class, summary is too brief, HDI-s, etc. will be added.
• The notebook at this moment is very much not complete, none of the Bayesian metalearners are included.

• a bigger one:
  In the X and the DR learner, the prediction of one learner is used in another model. At this moment, I calculate the mean of the first prediction and go with that, however that feels lazy. For example here

        # Split data to treated and untreated subsets
        X_t, y_t = X[treated == 1], y[treated == 1]
        X_u, y_u = X[treated == 0], y[treated == 0]


        # Estimate response function
        _fit(treated_model, X_t, y_t, coords)
        _fit(untreated_model, X_u, y_u, coords)


        pred_u_t = az.extract(
            untreated_model.predict(X_t), group="posterior_predictive", var_names="mu"
        ).mean(axis=1)
        pred_t_u = az.extract(
            treated_model.predict(X_u), group="posterior_predictive", var_names="mu"
        ).mean(axis=1)


        tau_t = y_t - pred_u_t
        tau_u = y_u - pred_t_u


        # Estimate CATE separately on treated and untreated subsets
        _fit(treated_cate_estimator, X_t, tau_t, coords)
        _fit(untreated_cate_estimator, X_u, tau_u, coords)

the posterior predictive pred_u_t should be used to construct the prior of tau_t imo. I guess I'll test things out.

I don't think I'll be able to work on this a lot this week unfortunately. I'll share where it lacks and what my plans are for (hopefully) the next week.

• Uplift, QINI and cumulative gain diagrams will be implemented, MetaLearner.plot should be rethought using these.
• In the PyMC class, summary is too brief, HDI-s, etc. will be added.
• The notebook at this moment is very much not complete, none of the Bayesian metalearners are included.

I think this is a fantastic job and I also had planned to explore this. Take your time. I hope to re-visit the latest changes soon.

Also, I think it should not be perfect. We can break down the ambitious proposal into some milestones.

@matekadlicsko let me know whenever you need another review (no rush)

@juanitorduz unfortunately I have a bit less time nowadays, but I'm progressing slowly.

I'm pretty much done with skl_meta_learners.py for now. I would be very interested in your opinion about summary.py. It is definitely not in its final form, but I would love to have some feedback.

The PyMC version is not yet ready for review unfortunately.

Sure! No rush! Take your time. I'll be off for a couple of weeks and come back for a review 🤜🤛

@matekadlicsko It looks like the work in summary.py that this PR will close #174. If so, would you be able to edit the issue to mention that so it auto closes?

EDIT: In fact, just a suggestion, but this PR might be trying to do a lot in one go. Feel free to break out the HTML summary output into a separate PR if you want. Up to you though :)