Changelog#

You can find the latest changes in the GitHub releases

Unreleased#

New Features#

  • Benchmark dataset loaders and ground-truth metrics (v1.0 M3). fetch_lalonde, fetch_ihdp and fetch_twins download a benchmark from its original source on first use and cache it under ~/causalml-data (CAUSALML_DATA overrides), with SHA256 verification so a file that has moved or been truncated raises instead of being parsed as data. download_if_missing=False stays offline, and clear_data_dir() empties the cache. None of the datasets are redistributed with CausalML; Benchmark Datasets records where each comes from and on what terms, including the four benchmarks that are documented rather than shipped.

    causalml.metrics gains the three metrics that need a known truth: pehe (Hill’s precision in estimating heterogeneous effects, with squared=False for the root), ate_error for the absolute error in the average effect, and policy_risk for the expected loss of the treat-if-positive policy on randomized data. Each docstring names what it requires — the ranking metrics stay the only ones computable on ordinary observational data.

    docs/examples/benchmark_leaderboard.ipynb is the leaderboard itself: running it end to end regenerates every published number. It reports three tables rather than one, because what is measurable is a property of how each dataset was made.

  • `CausalTreeRegressor` and `CausalRandomForestRegressor` accept ccp_alpha="cv" (#584). honesty=True supplies held-out leaf estimation; this setting adds the two remaining pieces of [4]. The splitting objective’s variance penalty is scaled by 1 + N_structure / N_estimation — the paper’s factor of 2 at an even split — and tree size is selected by cv_folds-fold cross-validation over the cost-complexity path, scoring each candidate subtree with that same objective evaluated on the held-out fold.

    Without it the tree grows until min_samples_leaf / min_group_samples stop it, and the variance penalty only ranks candidate splits instead of choosing tree size, which is the job it does in the paper. Held-out CATE RMSE for a single tree on a weak effect (16 seeds, min_samples_leaf=25, paired by seed):

    Noise

    honesty only

    ccp_alpha="cv"

    Change

    Leaves

    sigma=0.1

    0.1212

    0.1192

    -1.7% (n.s.)

    46 → 14

    sigma=0.5

    0.2128

    0.1676

    -21.2%

    46 → 13

    sigma=1.0

    0.3499

    0.2287

    -34.6%

    46 → 10

    sigma=2.0

    0.7011

    0.2977

    -57.5%

    46 → 8

    Better in 16 of 16 seeds at every noise level above 0.1, p < 0.0001; at sigma=0.1 there is no overfitting to remove and the two are equivalent. The cost is cv_folds extra fits per tree, roughly 5x fit time. Off by default because it changes fitted trees.

    This applies to a single tree. On CausalRandomForestRegressor the same option measured no gain: held-out CATE RMSE went from 0.084 to 0.130 at sigma=0.5 (+55%, 0 of 10 paired seeds better) and was unchanged at sigma=2.0. Averaging across trees already removes the variance the cross-validated pruning targets, so pruning each tree to a few leaves adds bias and reduces ensemble diversity, at a larger cost when noise is lower. Prefer it on CausalTreeRegressor.

    ccp_alpha="cv" requires honesty=True and raises otherwise, since the cross-validation scores candidate subtrees with the honest objective.

  • `CausalTreeRegressor.estimate_ate` and `BaseDRIVLearner.estimate_ate` accept pretrain (#517). pretrain=True estimates from the already-fitted model instead of refitting on X, so the rows passed in can be held out from the fit. The default refits on the rows it then estimates from, which leaves the estimate carrying whatever the model overfit. Fitting on half a randomized draw and estimating on the other half gave a mean absolute ATE error of 0.099 against 0.114 in-sample, over 20 seeds on CausalTreeRegressor.

    This completes the parameter across the package: the meta-learners have had it since #511. TMLELearner.estimate_ate is the remaining one without it and stays that way, since the class has no fit and so no fitted model to reuse.

    BaseDRIVLearner requires p when pretrain=True: the propensity stored by fit is indexed by the rows it was fit on, while the standard error indexes it by the rows passed here. assignment and pZ are only used to fit and go unused.

  • `UpliftTreeClassifier` can prune inside fit (#1003). prune_fraction (default None, off) holds out that fraction of the rows stratified on (treatment, outcome), grows the tree on the rest, and runs the existing prune() on the holdout with min_gain / prune_rule. Reaching validation-based pruning previously meant splitting the data, fitting, and calling prune() as a second step. prune() is unchanged for callers who manage their own holdout, and n_nodes_before_pruning_ records the size pruning started from.

    The pruning rows are removed before the honest split, so neither the split search nor the estimation half sees them. Pruning renumbers nodes and promotes internal nodes to leaves, so the honest re-estimation and the per-node group counts are redone against the pruned tree.

    fit now rejects a held-out fraction outside (0, 1) — prune_fraction on the uplift tree and estimation_sample_size on both the uplift and causal trees — raising a ValueError that names the parameter instead of train_test_split’s test_size. prune_fraction=0.0 was read as off.

    On make_uplift_classification (n=3000, 6 seeds, max_depth=None, min_samples_leaf=20), held-out qini went from -1.74 unpruned to -1.44 with prune_fraction=0.3, and to 0.83 combined with honesty=True. One simulated design, so the magnitudes are indicative.

Bug Fixes#

  • `CausalTreeRegressor.estimate_ate` understated the standard error (#1006). The interval came from dhat.std() / n, which divides by n rather than sqrt(n) and measures how the predicted effect varies across units instead of the sampling variability of its mean. At a nominal 95% over 100 randomized-trial draws (n=1000), the interval contained the true ATE 0 times.

    It is now the standard error of the estimate’s influence function, which adds the model’s residuals on the observed outcomes to the spread of the predicted effects — the quantity BaseTLearner.estimate_ate already computes as a three-term variance. Coverage on the same draws is 88%, the remainder being that estimate_ate fits and estimates on the same rows (#517). Point estimates are unchanged; only the interval moves, and it is wider.

  • `UpliftTreeClassifier.prune` left `_node_group_counts` stale (#1003). prune() replaced tree_ without rebuilding the per-node group counts, which are indexed by node id and read by the uplift-score p-value and the plot’s group_size. The stale array is longer than the pruned tree, so it returned another node’s counts instead of raising. prune() now carries the counts over to the pruned node ids, so a standalone call is fixed too, not only the fit-time path. A surviving node is reached by the same rows either way, so the values transfer unchanged; the remap replays the pruner’s traversal and matches a full recomputation exactly.

  • `uplift_tree_plot` failed with pyparsing 3.3.3 (#1052). Its graph was parsed by pydotplus, unmaintained since 2014, whose parser raises TypeError on the HTML-style node labels the function writes once pyparsing 3.3.3 is installed. The dependency is now pydot, the maintained library pydotplus was forked from; the returned pydot.Dot has the same create_png() / write_png() methods.

Behavior Changes#

  • `CausalTreeRegressor` and `CausalRandomForestRegressor` now estimate leaves honestly by default (#584). The new honesty parameter defaults to True: the sample is split in two, the tree structure is grown on one half and each leaf’s per-group outcome means are re-estimated on the other, so a leaf value no longer inherits the selection bias of the split search that produced it [4]. estimation_sample_size (default 0.5) sets the held-out fraction, and the split is stratified on treatment. Both are the names UpliftTreeClassifier already uses, and the default matches grf’s honesty = TRUE and EconML’s honest=True.

    This changes fitted models and predictions. An existing script gets a different tree, different leaf values and different CATE estimates without any edit. Pass honesty=False to keep the previous behavior.

    Measured over 200 replications on data with no treatment effect anywhere, with the tree structure held fixed across both arms: in-sample leaves reported a mean absolute estimated effect of 0.163 versus 0.095 for honest leaves, a 42% reduction in spurious heterogeneity. The trade is variance: each half sees only part of the data, so an honest tree is shallower and noisier per leaf, and at small sample sizes an individual estimate can be worse. Use honesty=False when halving the sample leaves too little to split on.

    On the forest, honesty composes with bootstrap rather than replacing it: the bootstrap counts arrive as each tree’s sample_weight and weight both the structure fit and the leaf re-estimation, so this is not the subsample-without-replacement scheme of [5].

  • `UpliftRandomForestClassifier` now defaults to n_jobs=None (one worker) instead of n_jobs=-1 (#991). Trees are fitted in parallel and each concurrent fit holds its own working set, so peak memory grew in proportion to the machine’s core count — the same fit needed more memory on a larger machine. On a 100k × 440 benchmark, peak memory was 8.8× the input array at n_jobs=-1 (10 cores) versus 1.4× at n_jobs=1.

    Fitted models and predictions are unchanged; only peak memory and wall time move. Existing code gets slower and much lighter without any edit. Pass n_jobs=-1 explicitly to restore the previous behavior. Note that n_estimators does not affect peak memory once n_jobs is fixed.

    This matches CausalRandomForestRegressor and scikit-learn’s forests, which use the same across-trees parallelism and default to serial.

  • `causalml.features.load_data` now returns numpy.ndarray instead of numpy.matrix (#978). This was the only function in the package that returned a numpy.matrix. Code that relied on matrix semantics — * as matrix multiplication, or an always-2-D result from indexing — needs np.asmatrix() around the call or, preferably, the equivalent ndarray operation. numpy.matrix carries a PendingDeprecationWarning upstream.

    The same change fixes an AssertionError raised whenever the one-hot encoder produced no columns: with an all-numeric feature list, a constant categorical column, or a high-cardinality column whose levels all fall below min_obs. All three now return a feature matrix instead of raising (#978, #979).

Deprecations#

  • Positional treatment and y are deprecated (#854). In v1.0 every learner’s positional argument order becomes fit(X, y, treatment, ...), matching the scikit-learn convention so a CausalML learner can be a Pipeline step. Passing treatment or y by position now emits a FutureWarning; positional order is unchanged in this release, so nothing breaks yet.

    The fix is to pass them by keyword — learner.fit(X=X, treatment=treatment, y=y) — which is order-independent and therefore correct both before and after the flip.

    This one cannot be caught at runtime: y and treatment are both same-length arrays, so a call left in the old positional form will silently train on swapped arguments at v1.0 rather than raising TypeError. See the migration guide for the per-family table and for the signatures that are not a plain swap (IVRegressor.fit and BaseDRIVLearner).

    The warning is live for three minor releases — 0.18.0, 0.19.0 and 0.20.0 — before the flip in v1.0, roughly nine months.

    By @jeongyoonlee in uber/causalml#975

Bug Fixes#

  • macOS without the OpenMP runtime now reports an actionable error (#908). The installation guide now documents the required OpenMP runtime and installation options.

Release Schedule#

Quarterly, targeting:

Release

Target

Argument order

0.18.0

September 2026

fit(X, treatment, y, ...) — deprecation warning added

0.19.0

December 2026

unchanged, warning continues

0.20.0

March 2027

unchanged, warning continues

1.0.0

June 2027

fit(X, y, treatment, ...) — the flip, shim removed

Dates are targets, not commitments. The ordering is fixed: no signature moves before v1.0.

0.17.0 (Jul 2026)#

  • Adds scikit-learn 1.9 support, resolving an import causalml.dataset failure present on the 0.16.0 wheel (#926).

  • Adds native Polars DataFrame/Series/LazyFrame support across all meta-learners, and a JAX/flax.nnx backend for DragonNet.

  • Makes the meta-learners scikit-learn compliant (BaseEstimator), so clone() and get_params() work.

  • Adds the RATE evaluation metric and post-fit confidence intervals for BaseTLearner, plus numerous bug fixes.

New Features#

  • Add native Polars DataFrame, Series, and LazyFrame support for all meta-learners by @aman-coder03 in uber/causalml#901

  • Polish Polars support and add bootstrap CI test coverage by @aman-coder03 in uber/causalml#921

  • Add JAX/flax.nnx backend for DragonNet by @xrhd in uber/causalml#918

  • docs: document JAX backend for DragonNet by @xrhd in uber/causalml#919

  • Make meta-learners scikit-learn compliant via BaseEstimator by @aman-coder03 in uber/causalml#912

  • Add Rank-weighted Average Treatment Effect (RATE) metric by @aman-coder03 in uber/causalml#887

  • Add rate_score() with bootstrap confidence intervals and p-values by @aman-coder03 in uber/causalml#890

  • Add AIPW docstring warning to get_toc() and rate_score() by @jeongyoonlee in uber/causalml#891

  • Add post-fit confidence intervals to BaseTLearner via store_bootstraps and return_ci by @aman-coder03 in uber/causalml#886

  • Support NaN values in UpliftTree and UpliftRandomForest by @aman-coder03 in uber/causalml#860

scikit-learn 1.9 Support#

  • Fix for compatibility with sklearn v1.9.0 by @jakevdp in uber/causalml#903

  • Fix CausalRandomForestRegressor.fit() on scikit-learn 1.9 by @jeongyoonlee in uber/causalml#907

  • Support CausalRandomForestRegressor.calculate_error() on scikit-learn >= 1.9 by @jeongyoonlee in uber/causalml#909

Bug Fixes#

  • Fix #904: Prevent deepcopy of fitted templates in bootstrap and correct predict validation ordering by @Saurav-Gupta-9741 in uber/causalml#910

  • Optimised training, inference and memory for metalearners in multitreatment settings by @Ic3fr0g in uber/causalml#896

  • Fix UpliftRandomForest predict shape mismatch with multiple treatments by @jeongyoonlee in uber/causalml#884

  • Fix uplift tree p-value NaN from division by zero by @jeongyoonlee in uber/causalml#882

  • Fix CausalRandomForestRegressor predicting inf from division by zero by @jeongyoonlee in uber/causalml#883

  • Fix SensitivityPlaceboTreatment ignoring actual treatment groups by @jeongyoonlee in uber/causalml#880

  • Fix seed parameter TypeError in BaseDRLearner bootstrap CI by @mohsinm-dev in uber/causalml#879

  • Fix ValueError on read-only arrays in BaseSLearner.predict() by @mohsinm-dev in uber/causalml#878

  • Add input validation to auuc_score for missing model columns by @jeongyoonlee in uber/causalml#881

  • Make xgboost optional in synthetic dataset generation by @Si-ra-kri in uber/causalml#872

  • Bug Fix: use iloc to index pd.Series by @bekojuniranjan in uber/causalml#877

Build / CI#

  • Make Cython line tracing opt-in to keep release wheels fast by @HSJung93 in uber/causalml#914

  • Remove the PyPI token from GitHub Actions in favor of the Trusted Publishing by @jeongyoonlee in uber/causalml#871

  • Upgrade GitHub Actions for Node 24 compatibility by @salmanmkc in uber/causalml#874

  • Upgrade GitHub Actions to latest versions by @salmanmkc in uber/causalml#875

  • ci: declare workflow-level contents: read on 4 workflows by @arpitjain099 in uber/causalml#900

Breaking Changes#

  • Meta-learner __init__ signatures (#912): to become scikit-learn BaseEstimator s, each learner now stores its constructor arguments verbatim and builds models in fit(). Most visibly, XGBRRegressor no longer accepts arbitrary **kwargs — pass XGBoost parameters via the explicit xgb_kwargs=<dict> argument.

0.16.0 (Feb 2026)#

  • BREAKING CHANGE: This release upgrades from manylinux2014 to manylinux_2_28 for Linux wheel distribution.

  • Pre-built wheels now require glibc 2.28 or later (Ubuntu 20.04+, RHEL 8+, Debian 10+).

  • Users on older Linux distributions (Ubuntu 18.04, RHEL 7, etc.) must build from source.

  • Removes scipy version pin, enabling compatibility with both scipy 1.16.x and 1.17.x.

Updates#

  • Upgrade to manylinux_2_28 and remove scipy version constraints by @jeongyoonlee in uber/causalml#869

  • Upgrade cibuildwheel to v3.3.1 and remove deprecated macos-13 runner by @jeongyoonlee in uber/causalml#867

  • Fix Ubuntu packaging failure - scipy manylinux compatibility by @jeongyoonlee in uber/causalml#865

  • Fix Ubuntu packaging failure by aligning cibuildwheel config with Python version requirement by @jeongyoonlee in uber/causalml#864

Breaking Changes#

  • Linux wheel compatibility: Pre-built wheels require glibc 2.28+ (manylinux_2_28)

    • ✅ Supported: Ubuntu 20.04+, RHEL/CentOS 8+, Debian 10+, Fedora 32+

    • ⚠️ Requires source build: Ubuntu 18.04, RHEL 7, Ubuntu 16.04, Debian 9

  • Python version: Minimum Python version is 3.11 (updated from 3.9)

Migration Notes#

If you are on an older Linux distribution:

  1. Check your glibc version: ldd --version

  2. If glibc < 2.28: Install from source instead of using pip wheels:

    git clone https://github.com/uber/causalml.git
cd causalml
pip install -e .

  3. Recommended: Upgrade to a modern Linux distribution (Ubuntu 20.04+, RHEL 8+)

0.15.1 (Apr 2024)#

  • This release fixes the build failure on macOS and a few bugs in UpliftTreeClassifier.

  • We have two new contributors, @lee-junseok and @IanDelbridge. Thanks for your contributions!

Updates#

New contributors#

0.15.0 (Feb 2024)#

  • In this release, we revamped documentation, cleaned up dependencies, and improved installation - in addition to the long list of bug fixes.

  • We have three new contributors, @peterloleungyau, @SuperBo, and @ZiJiaW, who submitted their first PRs to CausalML. @erikcs also contributed to @ras44’s PR #729 to add the wrapper for his MAQ implementation to CausalML. Thanks for your contributions!

Updates#

New contributors#

0.14.1 (Aug 2023)#

  • This release mainly addressed installation issues and updated documentation accordingly.

  • We have 4 new contributors. @bsaunders27, @xhulianoThe1, @zpppy, and @bsaunders23. Thanks for your contributions!

Updates#

New contributors#

0.14.0 (July 2023)#

  • CausalML surpassed 2MM downloads on PyPI and 4,100 stars on GitHub. Thanks for choosing CausalML and supporting us on GitHub.

  • We have 7 new contributors: @darthtrevino, @ras44, @AbhishekVermaDH, @joel-mcmurry, @AlxClt, @kklein, and @volico. Thanks for your contributions!

Updates#

  • Fix the readthedocs build failure by @jeongyoonlee in uber/causalml#545

  • Add pyproject.toml with basic build dependencies for PEP518 compliance by @darthtrevino in uber/causalml#553

  • bump numpy from 1.20.3 to 1.23.2 in environment-py38.yml #338 by @ras44 in uber/causalml#550

  • CausalTree split criterions fix and fit optimization by @alexander-pv in uber/causalml#557

  • fixing math notations for proper rendering by @AbhishekVermaDH in uber/causalml#558

  • Update methodology.rst by @joel-mcmurry in uber/causalml#568

  • Causal trees bootstrapping and max_leaf_nodes fixes with minor update by @alexander-pv in uber/causalml#583

  • Fix #596 by @AlxClt in uber/causalml#597

  • Add **kwargs to Explainer.plot_shap_values() by @jeongyoonlee in uber/causalml#603

  • Make the Adam optimization optional and learning rate/epochs configurable in DragonNet by @jeongyoonlee in uber/causalml#604

  • Fix bug in variance calculation in drivlearner. by @huigangchen in uber/causalml#606

  • Bug Fix in Dragonnet: Adam parameter name lr depreciation by @huigangchen in uber/causalml#617

  • Fix AttributeError in builds with numpy>=1.24 and pandas>=2.0 by @jeongyoonlee in uber/causalml#631

  • Pass on **kwargs in plot_shap_values of base meta leaner by @kklein in uber/causalml#627

  • Bump scipy from 1.4.1 to 1.10.0 by @dependabot in uber/causalml#629

  • Feature/ttest criterion by @volico in uber/causalml#570

  • Added Interaction Tree (IT), Causal Inference Tree (CIT), and Invariant DDP (IDDP) by @jroessler in uber/causalml#562

  • Causal trees option to return counterfactual outcomes by @alexander-pv in uber/causalml#623

New contributors#

0.13.0 (Sep 2022)#

  • CausalML surpassed 1MM downloads on PyPI and 3,200 stars on GitHub. Thanks for choosing CausalML and supporting us on GitHub.

  • We have 7 new contributors @saiwing-yeung, @lixuan12315, @aldenrogers, @vincewu51, @AlkanSte, @enzoliao, and @alexander-pv. Thanks for your contributions!

  • @alexander-pv revamped CausalTreeRegressor and added CausalRandomForestRegressor with more seamless integration with scikit-learn’s Cython tree module. He also added integration with shap for causal tree/ random forest interpretation. Please check out the example notebook.

  • We dropped the support for Python 3.6 and removed its test workflow.

Updates#

0.12.3 (Feb 2022)#

This patch is to release a version without the constraint for Shap to be abled to use for Conda.

Updates#

  • #483 by @ppstacy: Modify the requirement version of Shap

0.12.2 (Feb 2022)#

This patch includes three updates by @tonkolviktor and @heiderich as follows. We also start using black, a Python formatter. Please check out the updated contribution guideline to learn how to use it.

Updates#

  • #473 by @tonkolviktor: Open up the scipy dependency version

  • #476 by @heiderich: Use preferred backend for joblib instead of hard-coding it

  • #477 by @heiderich: Allow parallel prediction for UpliftRandomForestClassifier and make the joblib’s preferred backend configurable

0.12.1 (Feb 2022)#

This patch includes two bug fixes for UpliftRandomForestClassifier as follows:

Updates#

  • #462 by @paullo0106: Use the correct treatment_idx for fillTree() when applying validation data set

  • #468 by @jeongyoonlee: Switch the joblib backend for UpliftRandomForestClassifier to threading to avoid memory copy across trees

0.12.0 (Jan 2022)#

Updates#

  • Update documentation on Instrument Variable methods @huigangchen (#447)

  • Add benchmark simulation studies example notebook by @t-tte (#443)

  • Add sample_weight support for R-learner by @paullo0106 (#425)

  • Fix incorrect binning of numeric features in UpliftTreeClassifier by @jeongyoonlee (#420)

  • Update papers, talks, and publication info to README and refs.bib by @zhenyuz0500 (#410 #414 #433)

  • Add instruction for contributing.md doc by @jeongyoonlee (#408)

  • Fix incorrect feature importance calculation logic by @paullo0106 (#406)

  • Add parallel jobs support for NearestNeighbors search with n_jobs parameter by @paullo0106 (#389)

  • Fix bug in simulate_randomized_trial by @jroessler (#385)

  • Add GA pytest workflow by @ppstacy (#380)

0.11.0 (2021-07-28)#

Major Updates#

  • Make tensorflow dependency optional and add python 3.9 support by @jeongyoonlee (#343)

  • Add delta-delta-p (ddp) tree inference approach by @jroessler (#327)

  • Add conda env files for Python 3.6, 3.7, and 3.8 by @jeongyoonlee (#324)

Minor Updates#

  • Fix inconsistent feature importance calculation in uplift tree by @paullo0106 (#372)

  • Fix filter method failure with NaNs in the data issue by @manojbalaji1 (#367)

  • Add automatic package publish by @jeongyoonlee (#354)

  • Fix typo in unit_selection optimization by @jeongyoonlee (#347)

  • Fix docs build failure by @jeongyoonlee (#335)

  • Convert pandas inputs to numpy in S/T/R Learners by @jeongyoonlee (#333)

  • Require scikit-learn as a dependency of setup.py by @ibraaaa (#325)

  • Fix AttributeError when passing in Outcome and Effect learner to R-Learner by @paullo0106 (#320)

  • Fix error when there is no positive class for KL Divergence filter by @lleiou (#311)

  • Add versions to cython and numpy in setup.py for requirements.txt accordingly by @maccam912 (#306)

0.10.0 (2021-02-18)#

Major Updates#

  • Add Policy learner, DR learner, DRIV learner by @huigangchen (#292)

  • Add wrapper for CEVAE, a deep latent-variable and variational autoencoder based model by @ppstacy(#276)

Minor Updates#

  • Add propensity_learner to R-learner by @jeongyoonlee (#297)

  • Add BaseLearner class for other meta-learners to inherit from without duplicated code by @jeongyoonlee (#295)

  • Fix installation issue for Shap>=0.38.1 by @paullo0106 (#287)

  • Fix import error for sklearn>= 0.24 by @jeongyoonlee (#283)

  • Fix KeyError issue in Filter method for certain dataset by @surajiyer (#281)

  • Fix inconsistent cumlift score calculation of multiple models by @vaclavbelak (#273)

  • Fix duplicate values handling in feature selection method by @manojbalaji1 (#271)

  • Fix the color spectrum of SHAP summary plot for feature interpretations of meta-learners by @paullo0106 (#269)

  • Add IIA and value optimization related documentation by @t-tte (#264)

  • Fix StratifiedKFold arguments for propensity score estimation by @paullo0106 (#262)

  • Refactor the code with string format argument and is to compare object types, and change methods not using bound instance to static methods by @harshcasper (#256, #260)

0.9.0 (2020-10-23)#

  • CausalML won the 1st prize at the poster session in UberML’20

  • DoWhy integrated CausalML starting v0.4 (release note)

  • CausalML team welcomes new project leadership, Mert Bay

  • We have 4 new community contributors, Mario Wijaya (@mwijaya3), Harry Zhao (@deeplaunch), Christophe (@ccrndn) and Georg Walther (@waltherg). Thanks for the contribution!

Major Updates#

  • Add feature importance and its visualization to UpliftDecisionTrees and UpliftRF by @yungmsh (#220)

  • Add feature selection example with Filter methods by @paullo0106 (#223)

Minor Updates#

  • Implement propensity model abstraction for common interface by @waltherg (#223)

  • Fix bug in BaseSClassifier and BaseXClassifier by @yungmsh and @ppstacy (#217), (#218)

  • Fix parentNodeSummary for UpliftDecisionTrees by @paullo0106 (#238)

  • Add pd.Series for propensity score condition check by @paullo0106 (#242)

  • Fix the uplift random forest prediction output by @ppstacy (#236)

  • Add functions and methods to init for optimization module by @mwijaya3 (#228)

  • Install GitHub Stale App to close inactive issues automatically @jeongyoonlee (#237)

  • Update documentation by @deeplaunch, @ccrndn, @ppstacy(#214, #231, #232)

0.8.0 (2020-07-17)#

CausalML surpassed 100,000 downloads! Thanks for the support.

Major Updates#

  • Add value optimization to optimize by @t-tte (#183)

  • Add counterfactual unit selection to optimize by @t-tte (#184)

  • Add sensitivity analysis to metrics by @ppstacy (#199, #212)

  • Add the iv estimator submodule and add 2SLS model to it by @huigangchen (#201)

Minor Updates#

  • Add GradientBoostedPropensityModel by @yungmsh (#193)

  • Add covariate balance visualization by @yluogit (#200)

  • Fix bug in the X learner propensity model by @ppstacy (#209)

  • Update package dependencies by @jeongyoonlee (#195, #197)

  • Update documentation by @jeongyoonlee, @ppstacy and @yluogit (#181, #202, #205)

0.7.1 (2020-05-07)#

Special thanks to our new community contributor, Katherine (@khof312)!

Major Updates#

  • Adjust matching distances by a factor of the number of matching columns in propensity score matching by @yungmsh (#157)

  • Add TMLE-based AUUC/Qini/lift calculation and plotting by @ppstacy (#165)

Minor Updates#

  • Fix typos and update documents by @paullo0106, @khof312, @jeongyoonlee (#150, #151, #155, #163)

  • Fix error in UpliftTreeClassifier.kl_divergence() for pk == 1 or 0 by @jeongyoonlee (#169)

  • Fix error in BaseRRegressor.fit() without propensity score input by @jeongyoonlee (#170)

0.7.0 (2020-02-28)#

Special thanks to our new community contributor, Steve (@steveyang90)!

Major Updates#

  • Add a new nn inference submodule with DragonNet implementation by @yungmsh

  • Add a new feature selection submodule with filter feature selection methods by @zhenyuz0500

Minor Updates#

  • Make propensity scores optional in all meta-learners by @ppstacy

  • Replace eli5 permutation importance with sklearn’s by @yluogit

  • Replace ElasticNetCV with LogisticRegressionCV in propensity.py by @yungmsh

  • Fix the normalized uplift curve plot with negative ATE by @jeongyoonlee

  • Fix the TravisCI FOSSA error for PRs from forked repo by @steveyang90

  • Add documentation about tree visualization by @zhenyuz0500

0.6.0 (2019-12-31)#

Special thanks to our new community contributors, Fritz (@fritzo), Peter (@peterfoley) and Tomasz (@TomaszZamacinski)!

  • Improve UpliftTreeClassifier’s speed by 4 times by @jeongyoonlee

  • Fix impurity computation in CausalTreeRegressor by @TomaszZamacinski

  • Fix XGBoost related warnings by @peterfoley

  • Fix typos and improve documentation by @peterfoley and @fritzo

0.5.0 (2019-11-26)#

Special thanks to our new community contributors, Paul (@paullo0106) and Florian (@FlorianWilhelm)!

  • Add TMLELearner, targeted maximum likelihood estimator to inference.meta by @huigangchen

  • Add an option to DGPs for regression to simulate imbalanced propensity distribution by @huigangchen

  • Fix incorrect edge connections, and add more information in the uplift tree plot by @paullo0106

  • Fix an installation error related to Cython and numpy by @FlorianWilhelm

  • Drop Python 2 support from setup.py by @jeongyoonlee

  • Update causaltree.pyx Cython code to be compatible with scikit-learn>=0.21.0 by @jeongyoonlee

0.4.0 (2019-10-21)#

  • Add uplift_tree_plot() to inference.tree to visualize UpliftTreeClassifier by @zhenyuz0500

  • Add the Explainer class to inference.meta to provide feature importances using SHAP and eli5’s PermutationImportance by @yungmsh

  • Add bootstrap confidence intervals for the average treatment effect estimates of meta learners by @ppstacy

0.3.0 (2019-09-17)#

  • Extend meta-learners to support classification by @t-tte

  • Extend meta-learners to support multiple treatments by @yungmsh

  • Fix a bug in uplift curves and add Qini curves/scores to metrics by @jeongyoonlee

  • Add inference.meta.XGBRRegressor with early stopping and ranking optimization by @yluogit

0.2.0 (2019-08-12)#

  • Add optimize.PolicyLearner based on Athey and Wager 2017 [6]

  • Add the CausalTreeRegressor estimator based on Athey and Imbens 2016 [4] (experimental)

  • Add missing imports in features.py to enable label encoding with grouping of rare values in LabelEncoder()

  • Fix a bug that caused the mismatch between training and prediction features in inference.meta.tlearner.predict()

0.1.0 (unreleased)#

  • Initial release with the Uplift Random Forest, and S/T/X/R-learners.