v0.15.1

• 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!

What's Changed

• Relax pandas version requirement by @jeongyoonlee in #743
• Remove undefined variables in match.__main__() by @jeongyoonlee in #749
• Fix distr_plot_single_sim() by @jeongyoonlee in #750
• Add with_std, with_counts to create_table_one by @lee-junseok in #748
• fix stratified sampling call by @IanDelbridge in #756
• 20240207 honest leaf size by @IanDelbridge in #753
• 757: add return_ci=True in sensitivity by @lee-junseok in #758
• Update sensitivity tests with more meta-learners by @jeongyoonlee in #759
• manually specify multiprocessing use fork in setup.py by @IanDelbridge in #754
• v0.15.1 release by @jeongyoonlee in #760

New Contributors

• @lee-junseok made their first contribution in #748
• @IanDelbridge made their first contribution in #756

Full Changelog: v0.15.0...v0.15.1

v0.15.0

• In this release, we revamped documentation, cleaned up dependencies, and improved installation - in addition to the long list of bug fixes.
• We have four new contributors, @peterloleungyau, @SuperBo, @ZiJiaW, and @erikcs who submitted their first PRs to CausalML. Thanks for your contributions!

Updates

• Update python-publish.yml by @jeongyoonlee in #673
• Add build.[os, tools.python] to .readthedocs.yml by @jeongyoonlee in #676
• Update notebook example with causal trees interpretation by @alexander-pv in #683
• Remove the numpy and pandas version restriction in pyproject.toml by @jeongyoonlee in #681
• Add governance documents by @jeongyoonlee in #688
• Update GOVERNANCE.md by @ras44 in #691
• Dev/governance docs to snake-case by @ras44 in #693
• Reduce sklearn dependency in causalml by @alexander-pv in #686
• Update MAINTAINERS.md by @jeongyoonlee in #696
• Modified to speed up UpliftTreeClassifier.growDecisionTreeFrom. by @peterloleungyau in #695
• Update README.md by @ras44 in #698
• Add notebook examples to docs by @jeongyoonlee in #697
• resolves change requests in #166 by @ras44 in #701
• Fix the readthedocs build error by @jeongyoonlee in #702
• Replace Stack and PriorityHeap with cpp stack/heap methods in trees by @SuperBo in #700
• Hotfix for #701 by @jeongyoonlee in #705
• Dev/699 win build fix by @ras44 in #710
• expose n_jobs for rlearner by @ZiJiaW in #714
• minimal fix to resolve #707 by @ras44 in #720
• Add Python 3.10, 3.11, 3.12 to the testing by @cclauss in #454
• Remove Python 3.12 from the build tests in python-test.yaml by @jeongyoonlee in #726
• fix plot_std_diffs, add bal_tol, condense to one plot by @ras44 in #723
• Dev/677 documentation by @ras44 in #725
• documentation updates by @ras44 in #728
• resolves #730, docs clean conda install by @ras44 in #731
• minimal wrapper of MAQ #662 by @ras44 in #729
• Temporary fix for causal trees missing values support #733 by @alexander-pv in #734
• resolves #639, credit due to Dong Liu by @ras44 in #722
• v0.15.0 release by @jeongyoonlee in #737
• Remove maq git+pip dependency by @erikcs in #739

New Contributors

• @peterloleungyau made their first contribution in #695
• @SuperBo made their first contribution in #700
• @ZiJiaW made their first contribution in #714
• @erikcs made their first contribution in #739

Full Changelog: v0.14.1...v0.15.0

v0.14.1

• This release mainly addressed installation issues and updated documentation accordingly.
• We have 4 new contributors. @bsaunders27, @xhulianoThe1, @zpppy, and @bsaunders23. Thanks for your contributions!

What's Changed

• Update the python-publish workflow file to fix the package publish Gi… by @jeongyoonlee in #633
• Update Cython dependency by @alexander-pv in #640
• Fix for builds on Mac M1 infrastructure by @bsaunders27 in #641
• code cleanups by @xhulianoThe1 in #634
• support valid error early stopping by @zpppy in #614
• Revert "support valid error early stopping" by @jeongyoonlee in #645
• "support valid error early stopping" by @zpppy in #648
• fix: update to envs/ conda build for precompiled M1 installs by @bsaunders27 in #646
• Installation updates to README and .github/workflows by @ras44 in #637
• fix: simulate_randomized_trial by @bsaunders23 in #656
• issue 252 by @vincewu51 in #660
• ras44/651 graph viz, resolves #651 by @ras44 in #661
• linted with black by @ras44 in #663
• Fix issue 650 by @vincewu51 in #659
• Install graphviz in the workflow builds by @jeongyoonlee in #668
• Update docs/installation.rst by @jeongyoonlee in #667
• Schedule monthly PyPI install tests by @jeongyoonlee in #670
• v0.14.1 release by @jeongyoonlee in #672

New Contributors

• @bsaunders27 made their first contribution in #641
• @xhulianoThe1 made their first contribution in #634
• @zpppy made their first contribution in #614
• @bsaunders23 made their first contribution in #656

Full Changelog: v0.14.0...v0.14.1

v0.14.0

• 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. Welcome to the CausalML development team, and thanks for your contributions!

What's Changed

• Fix the readthedocs build failure by @jeongyoonlee in #545
• Add pyproject.toml with basic build dependencies for PEP518 compliance by @darthtrevino in #553
• bump numpy==1.23.2 in environment-py38.yml #338 by @ras44 in #550
• CausalTree split criterions fix and fit optimization by @alexander-pv in #557
• fixing math notations for proper rendering by @AbhishekVermaDH in #558
• Update methodology.rst by @joel-mcmurry in #568
• Causal trees bootstrapping and max_leaf_nodes fixes with minor update by @alexander-pv in #583
• Fix #596 by @AlxClt in #597
• Add **kwargs to Explainer.plot_shap_values() by @jeongyoonlee in #603
• Make the Adam optimization optional and learning rate/epochs configurable in DragonNet by @jeongyoonlee in #604
• Fix bug in variance calculation in drivlearner. by @huigangchen in #606
• Bug Fix in Dragonnet: Adam parameter name lr depreciation by @huigangchen in #617
• Fix AttributeError in builds with numpy>=1.24 and pandas>=2.0 by @jeongyoonlee in #631
• Pass on **kwargs in plot_shap_values of base meta leaner by @kklein in #627
• Bump scipy from 1.4.1 to 1.10.0 by @dependabot in #629
• Feature/ttest criterion by @volico in #570
• Added Interaction Tree (IT), Causal Inference Tree (CIT), and Invariant DDP (IDDP) by @jroessler in #562
• Causal trees option to return counterfactual outcomes by @alexander-pv in #623
• Up the version to 0.14 by @jeongyoonlee in #632

New Contributors

• @darthtrevino made their first contribution in #553
• @ras44 made their first contribution in #550
• @AbhishekVermaDH made their first contribution in #558
• @joel-mcmurry made their first contribution in #568
• @AlxClt made their first contribution in #597
• @kklein made their first contribution in #627
• @volico made their first contribution in #570

Full Changelog: v0.13.0...v0.14.0

v0.13.0

• 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.

What's Changed

• Fix typo (% -> $) by @saiwing-yeung in #488
• Add function for calculating PNS bounds by @t-tte in #482
• Fix hard coding bug by @t-tte in #492
• Update README of conda install and instruction of maintain in conda-forge by @ppstacy in #485
• Update examples.rst by @lixuan12315 in #496
• Fix incorrect effect_learner_objective in XGBRRegressor by @jeongyoonlee in #504
• Fix Filter F doesn't work with latest statsmodels' F test f-value format by @paullo0106 in #505
• Exclude tests in setup.py by @aldenrogers in #508
• Enabling higher orders feature importance for F filter and LR filter by @zhenyuz0500 in #509
• Ate pretrain 0506 by @vincewu51 in #511
• Update methodology.rst by @AlkanSte in #518
• Fix the bug of incorrect result in qini for multiple models by @enzoliao in #520
• Test get_qini() by @enzoliao in #523
• Fixed typo in uplift_trees_with_synthetic_data.ipynb by @jroessler in #531
• Remove Python 3.6 test from workflows by @jeongyoonlee in #535
• Causal trees update by @alexander-pv in #522
• Causal trees interpretation example by @alexander-pv in #536

New Contributors

• @saiwing-yeung made their first contribution in #488
• @lixuan12315 made their first contribution in #496
• @aldenrogers made their first contribution in #508
• @vincewu51 made their first contribution in #511
• @AlkanSte made their first contribution in #518
• @enzoliao made their first contribution in #520
• @alexander-pv made their first contribution in #522

Full Changelog: v0.12.3...v0.13.0

v0.12.3

This patch is to release a version without the constraint of Shap which can be used for conda-forge.

What's Changed

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

Full Changelog: v0.12.2...v0.12.3

v0.12.2

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

What's Changed

• Opens up scipy dependency version range towards newer releases (#441) by @tonkolviktor in #473
• Merely define preferred backend for joblib instead of hard-coding it by @heiderich in #476
• Allow parallel prediction and make joblib's backend configurable for UpliftRandomForestClassifier by @heiderich in #477
• Reformat code using black by @jeongyoonlee in #474

New Contributors

• @tonkolviktor made their first contribution in #473
• @heiderich made their first contribution in #476

Full Changelog: v0.12.1...v0.12.2

v0.12.1

This patch includes two bug fixes for UpliftRandomForestClassifier as follows:

• #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

v0.12.0

0.12.0 (Jan 2022)

• CausalML surpassed 637K downloads on PyPI and 2,500 stars on Github!
• We have 4 new community contributors, Luis (@lgmoneda ), Ravi (@raviksharma), Louis (@LouisHernandez17) and JackRab (@Jackrab). Thanks for the contribution!
• We refactored and speeded up UpliftTreeClassifier/UpliftRandomForestClassifier by 5x with Cython (#422 #440 by @jeongyoonlee)
• We revamped our API documentation, it now includes the latest methodology, references, installation, notebook examples, and graphs! (#413 by @huigangchen @t-tte @zhenyuz0500 @jeongyoonlee @paullo0106)
• Our team gave talks at 2021 Conference on Digital Experimentation @ MIT (CODE@MIT), Causal Data Science Meeting 2021, and KDD 2021 Tutorials on CausalML introduction and applications. Please take a look if you missed them! Full list of publications and talks can be found here.

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)

v0.11.1

Merge pull request #380 from uber/GA_pytest

Add GA pytest workflow