mlsanityData to 7 Oct 2026

CausalML: Python Package for Causal Machine Learning

arXiv 2002.11631 ↗First posted 25 Feb 2020cs.CY cs.LG stat.COcited by 74

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Abstract (arXiv)

CausalML is a Python implementation of algorithms related to causal inference and machine learning. Algorithms combining causal inference and machine learning have been a trending topic in recent years. This package tries to bridge the gap between theoretical work on methodology and practical applications by making a collection of methods in this field available in Python. This paper introduces the key concepts, scope, and use cases of this package.

Key facts. Cited by 74 papers in the map (data through 7 Oct 2026). First posted on arXiv 25 Feb 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 10; 2022: 11; 2023: 17; 2024: 14; 2025: 12; 2026: 10.

74 papers.

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  1. 2021gCastle: A Python Toolbox for Causal Discoverycited by 60
  2. 2022DoWhy-GCM: An extension of DoWhy for causal inference in graphical causal modelscited by 40
  3. 2021DoubleML -- An Object-Oriented Implementation of Double Machine Learning in Pythoncited by 23
  4. 2022Evaluation Methods and Measures for Causal Learning Algorithmscited by 23
  5. 2021Interventional Sum-Product Networks: Causal Inference with Tractable Probabilistic Modelscited by 22
  6. 2022Direct Heterogeneous Causal Learning for Resource Allocation Problems in Marketingcited by 21
  7. 2021DoWhy: Addressing Challenges in Expressing and Validating Causal Assumptionscited by 20
  8. 2023An Adaptive Kernel Approach to Federated Learning of Heterogeneous Causal Effectscited by 19
  9. 2025CounterBench: Evaluating and Improving Counterfactual Reasoning in Large Language Modelscited by 19
  10. 2023A Review of the Role of Causality in Developing Trustworthy AI Systemscited by 14
  11. 2021A Large Scale Benchmark for Individual Treatment Effect Prediction and Uplift Modelingcited by 12
  12. 2022Towards assessing agricultural land suitability with causal machine learningcited by 11
38 more
  1. 2023LLM4Causal: Democratized Causal Tools for Everyone via Large Language Modelcited by 9
  2. 2022Evaluating Digital Agriculture Recommendations with Causal Inferencecited by 7
  3. 2024Entire Chain Uplift Modeling with Context-Enhanced Learning for Intelligent Marketingcited by 7
  4. 2026FML-bench: A Controlled Study of AI Research Agent Strategies from the Perspective of Search Dynamicscited by 6
  5. 2021CausalNLP: A Practical Toolkit for Causal Inference with Textcited by 5
  6. 2023Causal Inference for Banking Finance and Insurance A Surveycited by 5
  7. 2021Federated Estimation of Causal Effects from Observational Datacited by 4
  8. 2021Heterogeneous Treatment Effect Estimation using machine learning for Healthcare application: tutorial and benchmarkcited by 4
  9. 2022A Free Lunch with Influence Functions? Improving Neural Network Estimates with Concepts from Semiparametric Statisticscited by 4
  10. 2023Selecting Robust Features for Machine Learning Applications using Multidata Causal Discoverycited by 4
  11. 2022Generalized Causal Tree for Uplift Modelingcited by 3
  12. 2023Incremental Profit per Conversion: a Response Transformation for Uplift Modeling in E-Commerce Promotionscited by 3
  13. 2024Uplift Modeling Under Limited Supervisioncited by 3
  14. 2024Benchmarking for Deep Uplift Modeling in Online Marketingcited by 3
  15. 2025Robust Uplift Modeling with Large-Scale Contexts for Real-time Marketingcited by 3
  16. 2025Reimagining Urban Science: Scaling Causal Inference with Large Language Modelscited by 3
  17. 2025Adjustment for Confounding using Pre-Trained Representationscited by 3
  18. 2025FML-bench: Benchmarking Machine Learning Agents for Scientific Researchcited by 3
  19. 2021To do or not to do: cost-sensitive causal decision-makingcited by 2
  20. 2022Evaluating Digital Tools for Sustainable Agriculture using Causal Inferencecited by 2
  21. 2023Understanding the impacts of crop diversification in the context of climate change: a machine learning approachcited by 2
  22. 2023Fairness Implications of Heterogeneous Treatment Effect Estimation with Machine Learning Methods in Policy-makingcited by 2
  23. 2023OpportunityFinder: A Framework for Automated Causal Inferencecited by 2
  24. 2024Causality for Earth Science -- A Review on Time-series and Spatiotemporal Causality Methodscited by 2
  25. 2024Iterative Causal Segmentation: Filling the Gap between Market Segmentation and Marketing Strategycited by 2
  26. 2025Beyond Predictions: A Participatory Framework for Multi-Stakeholder Decision-Makingcited by 2
  27. 2022A Double Machine Learning Trend Model for Citizen Science Datacited by 1
  28. 2022On How AI Needs to Change to Advance the Science of Drug Discoverycited by 1
  29. 2023balance -- a Python package for balancing biased data samplescited by 1
  30. 2023VISPUR: Visual Aids for Identifying and Interpreting Spurious Associations in Data-Driven Decisionscited by 1
  31. 2023Assessing the Causal Impact of Humanitarian Aid on Food Securitycited by 1
  32. 2024IncomeSCM: From tabular data set to time-series simulator and causal estimation benchmarkcited by 1
  33. 2024Improving Bias Correction Standards by Quantifying its Effects on Treatment Outcomescited by 1
  34. 2024Causal Feature Selection Method for Contextual Multi-Armed Bandits in Recommender Systemcited by 1
  35. 2024Ads Supply Personalization via Doubly Robust Learningcited by 1
  36. 2025C-HDNet: Hyperdimensional Computing for Causal Effect Estimation from Observational Data Under Network Interferencecited by 1
  37. 2025ORCA: ORchestrating Causal Agentcited by 1
  38. 2026De-paradox Tree: Breaking Down Simpson's Paradox via A Kernel-Based Partition Algorithmcited by 1

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