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Do you have any example showing how PyMC-BART can be used for causal inference? Do you have plans for implementing features similar to the [bartCause](https://cran.r-project.org/web/packages/bartCause/index.html) R package?

Greetings all, I know BART is relatively new, but I wonder if it is possible to apply BART to categorical (string) variables. Simply factorizing variables would do the trick? How...

Hi! Thanks for creating this great package :) I think one important aspect of understanding models is the ability to explore conditional posteriors. In the [tutorial](https://www.pymc.io/projects/bart/en/latest/examples/BART_introduction.html#) you mention the `kind="ice"`...

Following #31 we need to better explain what BART is doing, in particular some details of the latest implementation.

documentation

We currently mention that X is "a covariate matrix", and Y a "response vector", but we could be more explicit about the expected shapes.

## Short Description Hi all, I'm trying to run the first BART model example from Bayesian Analysis with Python in Google Colab. I'm getting some errors that I think are...

updates: - [github.com/astral-sh/ruff-pre-commit: v0.5.4 → v0.5.5](https://github.com/astral-sh/ruff-pre-commit/compare/v0.5.4...v0.5.5)

Its often useful to be able to model known monotonic relationships; using monotone trees is one way of doing this. ## Thoughts on implementation Some inspiration [here](https://projecteuclid.org/journals/bayesian-analysis/advance-publication/mBART-Multidimensional-Monotone-BART/10.1214/21-BA1259.full)

updates: - [github.com/astral-sh/ruff-pre-commit: v0.14.1 → v0.14.2](https://github.com/astral-sh/ruff-pre-commit/compare/v0.14.1...v0.14.2)

We need to follow the procedure explained in https://discord.com/channels/974333176623280130/1367480257468432424/1371480480976998512