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StochTree Python API Demo#

The following demos showcase (some of) the functionality and output of the stochtree python package.

  1. Supervised Learning: using BARTModel() for classic supervised learning tasks
  2. Causal Inference: using BCFModel() for causal effect estimation
  3. Heteroskedastic Supervised Learning: using BARTModel() for supervised learning tasks with heteroskedasticity (covariate-dependent variance)
  4. Multivariate Treatment Causal Inference: using BCFModel() for causal effect estimation with a multivariate (continuous) treatment variable
  5. Model Serialization: saving and reloading stochtree models via JSON
  6. Internal Tree Inspection: inspecting the trees in a sampled stochtree forest
  7. Low-Level Interface: using the low-level stochtree interface to construct a custom sampling loop