# CausalML: Python Package for Causal Machine Learning

arXiv: https://arxiv.org/abs/2002.11631 · HTML: https://mlsanity.com/paper/2002.11631/

## Key facts

- Cited by 74 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Feb 2020.

## Built on by (most cited first, 30 of 74)

- [gCastle: A Python Toolbox for Causal Discovery](https://mlsanity.com/paper/2111.15155/) (2021)
- [DoWhy-GCM: An extension of DoWhy for causal inference in graphical causal models](https://mlsanity.com/paper/2206.06821/) (2022)
- [DoubleML -- An Object-Oriented Implementation of Double Machine Learning in Python](https://mlsanity.com/paper/2104.03220/) (2021)
- [Evaluation Methods and Measures for Causal Learning Algorithms](https://mlsanity.com/paper/2202.02896/) (2022)
- [Interventional Sum-Product Networks: Causal Inference with Tractable Probabilistic Models](https://mlsanity.com/paper/2102.10440/) (2021)
- [Direct Heterogeneous Causal Learning for Resource Allocation Problems in Marketing](https://mlsanity.com/paper/2211.15728/) (2022)
- [CounterBench: Evaluating and Improving Counterfactual Reasoning in Large Language Models](https://mlsanity.com/paper/2502.11008/) (2025)
- [DoWhy: Addressing Challenges in Expressing and Validating Causal Assumptions](https://mlsanity.com/paper/2108.13518/) (2021)
- [An Adaptive Kernel Approach to Federated Learning of Heterogeneous Causal Effects](https://mlsanity.com/paper/2301.00346/) (2023)
- [A Review of the Role of Causality in Developing Trustworthy AI Systems](https://mlsanity.com/paper/2302.06975/) (2023)
- [A Large Scale Benchmark for Individual Treatment Effect Prediction and Uplift Modeling](https://mlsanity.com/paper/2111.10106/) (2021)
- [Towards assessing agricultural land suitability with causal machine learning](https://mlsanity.com/paper/2204.12956/) (2022)
- [LLM4Causal: Democratized Causal Tools for Everyone via Large Language Model](https://mlsanity.com/paper/2312.17122/) (2023)
- [FML-bench: A Controlled Study of AI Research Agent Strategies from the Perspective of Search Dynamics](https://mlsanity.com/paper/2605.17373/) (2026)
- [Evaluating Digital Agriculture Recommendations with Causal Inference](https://mlsanity.com/paper/2211.16938/) (2022)
- [Entire Chain Uplift Modeling with Context-Enhanced Learning for Intelligent Marketing](https://mlsanity.com/paper/2402.03379/) (2024)
- [CausalNLP: A Practical Toolkit for Causal Inference with Text](https://mlsanity.com/paper/2106.08043/) (2021)
- [Causal Inference for Banking Finance and Insurance A Survey](https://mlsanity.com/paper/2307.16427/) (2023)
- [Federated Estimation of Causal Effects from Observational Data](https://mlsanity.com/paper/2106.00456/) (2021)
- [Heterogeneous Treatment Effect Estimation using machine learning for Healthcare application: tutorial and benchmark](https://mlsanity.com/paper/2109.12769/) (2021)
- [A Free Lunch with Influence Functions? Improving Neural Network Estimates with Concepts from Semiparametric Statistics](https://mlsanity.com/paper/2202.09096/) (2022)
- [Selecting Robust Features for Machine Learning Applications using Multidata Causal Discovery](https://mlsanity.com/paper/2304.05294/) (2023)
- [Adjustment for Confounding using Pre-Trained Representations](https://mlsanity.com/paper/2506.14329/) (2025)
- [FML-bench: Benchmarking Machine Learning Agents for Scientific Research](https://mlsanity.com/paper/2510.10472/) (2025)
- [Generalized Causal Tree for Uplift Modeling](https://mlsanity.com/paper/2202.02416/) (2022)
- [Incremental Profit per Conversion: a Response Transformation for Uplift Modeling in E-Commerce Promotions](https://mlsanity.com/paper/2306.13759/) (2023)
- [Uplift Modeling Under Limited Supervision](https://mlsanity.com/paper/2403.19289/) (2024)
- [Benchmarking for Deep Uplift Modeling in Online Marketing](https://mlsanity.com/paper/2406.00335/) (2024)
- [Robust Uplift Modeling with Large-Scale Contexts for Real-time Marketing](https://mlsanity.com/paper/2502.15697/) (2025)
- [Reimagining Urban Science: Scaling Causal Inference with Large Language Models](https://mlsanity.com/paper/2504.12345/) (2025)

## 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.