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uber/causalml Contributions

PR #887 – Add Rank-weighted Average Treatment Effect (RATE) Metric
Merged: Mar 2026

Added causalml/metrics/rate.py with three public functions — get_toc(), rate_score(), and plot_toc() — following the exact same API conventions as get_qini / qini_score / plot_qini
get_toc() computes the Targeting Operator Characteristic curve via O(n) cumulative sums; rate_score() computes the RATE scalar with AUTOC (1/q) or Qini (q) weighting; plot_toc() visualizes the TOC curve
Supported both oracle mode (simulated tau) and observed RCT mode (y + w); fixed normalize division-by-zero by using max(|TOC|) instead of TOC(1); added logger.warning for observed-outcome fallback
Added 20 unit tests in tests/test_rate.py; addressed all blocking and non-blocking review comments across two review rounds; passed black and pre-commit clean
Implementation verified correct against the Yadlowsky et al. (2021) paper and the grf R package reference by the maintainer

PR #860 – Add Native NaN Support for UpliftTree and UpliftRandomForest
Merged: Mar 2026

Added native NaN routing logic to each candidate split, evaluating both left/right directions and learning the optimal routing per node — consistent with scikit-learn's decision tree behavior
Stored the learned NaN routing in each DecisionTree node and applied it consistently during training, pruning, filling, and prediction
Guarded all np.isnan() calls with np.issubdtype(..., np.number) to prevent TypeError on string/categorical columns
Added NaN-aware percentile calculation by filtering out NaN values before computing split thresholds
Added two targeted tests: one for NaN values in numeric columns, one for None values in object-dtype columns