Causal Forest

Causal forests are machine learning methods designed to estimate heterogeneous treatment effects—how the impact of an intervention varies across different subgroups. Current research emphasizes improving the interpretability of these models, often by distilling complex forest structures into simpler, more easily understood representations like causal trees or rule sets, while maintaining accuracy. This focus on interpretability is driven by the need for transparency and accountability in high-stakes applications, such as personalized medicine and policy evaluation, where understanding *why* a treatment works for certain groups is crucial. The development of efficient algorithms for large datasets and extensions to handle longitudinal data and continuous treatments are also active areas of investigation.

Papers

August 27, 2024

Causal Rule Forest: Toward Interpretable and Precise Treatment Effect Estimation
Chan Hsu, Jun-Ting Wu, Yihuang Kang
Treatment Effect Conditional Average Treatment Effect Interpretable Causal Heterogeneity Challenge Causal Forest

August 2, 2024

Distilling interpretable causal trees from causal forests
Patrick Rehill
Conditional Average Treatment Effect Causal Forest Causal Feature Distillation

July 16, 2024

Bayesian Causal Forests for Longitudinal Data: Assessing the Impact of Part-Time Work on Growth in High School Mathematics Achievement
Nathan McJames, Ann O'Shea, Andrew Parnell
Causal Effect Heterogeneous Treatment Effect Longitudinal Data Longitudinal Study Causal Forest

May 6, 2024

Deep Learning for Causal Inference: A Comparison of Architectures for Heterogeneous Treatment Effect Estimation
Demetrios Papakostas, Andrew Herren, P. Richard Hahn, Francisco Castillo
Deep Learning Causal Inference Consistent Comparison Heterogeneous Treatment Effect Architecture Perspective Causal Forest Heterogeneous Treatment Effect Estimation

October 20, 2023

Transparency challenges in policy evaluation with causal machine learning -- improving usability and accountability
Patrick Rehill, Nicholas Biddle
Policy Evaluation Transparency Index Causal Machine Learning Important Usability Challenge Causal Machine Causal Forest

October 10, 2023

Causal Rule Learning: Enhancing the Understanding of Heterogeneous Treatment Effect via Weighted Causal Rules
Ying Wu, Hanzhong Liu, Kai Ren, Xiangyu Chang
Human Understanding Heterogeneous Treatment Effect Causal Assumption Causal Forest

September 26, 2023

Targeting relative risk heterogeneity with causal forests
Vik Shirvaikar, Xi Lin, Chris Holmes
Heterogeneous Treatment Effect Generalized Linear Model Unobserved Heterogeneity Causal Forest Disease Heterogeneity

March 8, 2023

Bayesian Causal Forests for Multivariate Outcomes: Application to Irish Data From an International Large Scale Education Assessment
Nathan McJames, Andrew Parnell, Yong Chen Goh, Ann O'Shea
Application Proficiency Large Scale Causal Effect Available Datasets Causal Machine Learning Causal Forest Bayesian Additive Regression Tree

February 3, 2023

Causal Inference Based Single-branch Ensemble Trees For Uplift Modeling
Fanglan Zheng, Menghan Wang, Kun Li, Jiang Tian, Xiaojia Xiang
Causal Inference Uplift Modeling Tree Based Ensemble Causal Forest Inference Rule Predictive Probability

September 15, 2022

Stochastic Tree Ensembles for Estimating Heterogeneous Effects
Nikolay Krantsevich, Jingyu He, P. Richard Hahn
Causal Inference Tree Ensemble Confounding Bias Heterogeneous Effect Gibbs Sampler Causal Forest

August 29, 2022

Supporting Medical Relation Extraction via Causality-Pruned Semantic Dependency Forest
Yifan Jin, Jiangmeng Li, Zheng Lian, Chengbo Jiao, Xiaohui Hu
Relation Extraction Dependency Tree Biomedical Relation Extraction Causal Forest Explainable Causal

April 14, 2022

Learning Optimal Dynamic Treatment Regimes Using Causal Tree Methods in Medicine
Theresa Blümlein, Joel Persson, Stefan Feuerriegel
Time Varying Dynamic Treatment Regime Causal Forest Sequential Treatment Causality Method

March 21, 2022

GCF: Generalized Causal Forest for Heterogeneous Treatment Effect Estimation in Online Marketplace
Shu Wan, Chen Zheng, Zhonggen Sun, Mengfan Xu, Xiaoqing Yang, Hongtu Zhu, Jiecheng Guo
Causal Inference Heterogeneous Treatment Effect Online Marketplace Dose Response Causal Forest Heterogeneous Treatment Effect Estimation Continuous Treatment Effect

January 29, 2022

LBCF: A Large-Scale Budget-Constrained Causal Forest Algorithm
Meng Ai, Biao Li, Heyang Gong, Qingwei Yu, Shengjie Xue, Yuan Zhang, Yunzhou Zhang, Peng Jiang
Institutional Incentive Treatment Assignment Causal Forest

Causal Forest

Papers

Causal Rule Forest: Toward Interpretable and Precise Treatment Effect Estimation

Distilling interpretable causal trees from causal forests

Bayesian Causal Forests for Longitudinal Data: Assessing the Impact of Part-Time Work on Growth in High School Mathematics Achievement

Deep Learning for Causal Inference: A Comparison of Architectures for Heterogeneous Treatment Effect Estimation

Transparency challenges in policy evaluation with causal machine learning -- improving usability and accountability

Causal Rule Learning: Enhancing the Understanding of Heterogeneous Treatment Effect via Weighted Causal Rules

Targeting relative risk heterogeneity with causal forests

Bayesian Causal Forests for Multivariate Outcomes: Application to Irish Data From an International Large Scale Education Assessment

Causal Inference Based Single-branch Ensemble Trees For Uplift Modeling

Stochastic Tree Ensembles for Estimating Heterogeneous Effects

Supporting Medical Relation Extraction via Causality-Pruned Semantic Dependency Forest

Learning Optimal Dynamic Treatment Regimes Using Causal Tree Methods in Medicine

GCF: Generalized Causal Forest for Heterogeneous Treatment Effect Estimation in Online Marketplace

LBCF: A Large-Scale Budget-Constrained Causal Forest Algorithm