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Implements the Causal Forest algorithm formulated in Athey and Wager (2018).

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hi when I tried to install the package in the coda, here is the issue I face: the Solving environment: failed with repodata from current_repodata.json, will retry with next repodata...

I am able to run your code when I manually set num_workers = 1, but anytime I try to use parallel, I get the following: File "", line 1, in...

Add unit tests to all functions in `causaltree.py`.

good first issue
high-priority

Rename python package from `causal_forest` to `cforest`.

low-priority

Change API to sklearn standard --even though treatment effect estimation is a mix between supervised and unsupervised learning and therefore any estimator can never utilize the features of sklearn it...

enhancement
low-priority

``` --> 447 left = sorted_subset_index[: (split_index + 1)] 448 right = sorted_subset_index[(split_index + 1) :] 449 nonzero_index = np.nonzero(index)[0] TypeError: unsupported operand type(s) for +: 'NoneType' and 'int' ```

bug
high-priority

**Problem:** Right now the function `_find_optimal_split` is very inefficient. In the inner loop over `splitting_points` I compute means and sums in every iteration, even though I could update an initial...

enhancement
low-priority

Add honesty feature to fitted tree as formulated in Athey and Imbens (2016) and Athey and Wager (2018).

enhancement
high-priority