Tools for finding heterogeneous treatment effects (and means) based on partitioning the covariate/feature space via full cross-cuts and solved via greedy search. A typical usage would be analyzing an experiment to find the high-level subgroups (a coarse partition that is useful to humans) that differ in their estimated treatment effects.
This package is inspired by, and uses ideas from, Causal Tree but aims to have the partition be more interpretable and have better accuracy. It is slower, though for high-level partitions this is usually not an issue.
This project is currently in an advanced prototype stage. Issues may still be found in common usage. Please create issues for these!
Documentation can be found online here (and in the package).
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