causal-forest
causal-forest copied to clipboard
Implements the Causal Forest algorithm formulated in Athey and Wager (2018).
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`.
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...
``` --> 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' ```
**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...
Add honesty feature to fitted tree as formulated in Athey and Imbens (2016) and Athey and Wager (2018).