Heterogeneous Treatment Effect

Heterogeneous treatment effects (HTE) research focuses on identifying how the impact of a treatment varies across individuals or subgroups, moving beyond average treatment effects. Current research emphasizes developing robust and efficient methods for estimating conditional average treatment effects (CATEs), employing various machine learning models such as Bayesian Causal Forests, gradient boosting trees, and meta-learners, often incorporating techniques to address confounding and covariate shift. This field is crucial for advancing personalized interventions in diverse areas like medicine, policy, and marketing, enabling more targeted and effective strategies based on individual characteristics.

Papers

February 3, 2024

Combining T-learning and DR-learning: a framework for oracle-efficient estimation of causal contrasts
Lars van der Laan, Marco Carone, Alex Luedtke
New Framework Empirical Risk Minimization Heterogeneous Treatment Effect Conditional Average Treatment Effect Combining Machine Learning

January 30, 2024

Causal Machine Learning for Cost-Effective Allocation of Development Aid
Milan Kuzmanovic, Dennis Frauen, Tobias Hatt, Stefan Feuerriegel
Heterogeneous Treatment Effect Causal Machine Learning AID User Dose Response Efficient Allocation Allocation Mechanism HIV Surveillance

January 20, 2024

Identification and Estimation of Conditional Average Partial Causal Effects via Instrumental Variable
Yuta Kawakami, Manabu Kuroki, Jin Tian
Estimation Task Person Identification Causal Effect Heterogeneous Treatment Effect Conditional Average Treatment Effect Instrumental Variable Unobserved Heterogeneity

January 1, 2024

Inferring Heterogeneous Treatment Effects of Crashes on Highway Traffic: A Doubly Robust Causal Machine Learning Approach
Shuang Li, Ziyuan Pu, Zhiyong Cui, Seunghyeon Lee, Xiucheng Guo, Dong Ngoduy
Causal Effect Structural Causal Model Heterogeneous Treatment Effect Causal Machine Learning Urban Traffic CRASH Bug Robust Causal

December 16, 2023

Uncertainty Quantification in Heterogeneous Treatment Effect Estimation with Gaussian-Process-Based Partially Linear Model
Shunsuke Horii, Yoichi Chikahara
Gaussian Process Uncertainty Quantification Treatment Effect Heterogeneous Treatment Effect Prior Distribution Semi Parametric Heterogeneous Treatment Effect Estimation Bayesian Inference Framework

November 10, 2023

Business Policy Experiments using Fractional Factorial Designs: Consumer Retention on DoorDash
Yixin Tang, Yicong Lin, Navdeep S. Sahni
Heterogeneous Treatment Effect Big Gain Customer Retention Factorial Design

November 1, 2023

Personalized Assignment to One of Many Treatment Arms via Regularized and Clustered Joint Assignment Forests
Rahul Ladhania, Jann Spiess, Lyle Ungar, Wenbo Wu
Heterogeneous Treatment Effect Treatment Assignment Multi Type Galton Watson Forest Many Arm

October 12, 2023

Machine Learning Who to Nudge: Causal vs Predictive Targeting in a Field Experiment on Student Financial Aid Renewal
Susan Athey, Niall Keleher, Jann Spiess
Machine Learning Baseline Result Structured Dropout Heterogeneous Treatment Effect Causal Event Causal Approach Field Experiment

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

June 20, 2023

Should I Stop or Should I Go: Early Stopping with Heterogeneous Populations
Hammaad Adam, Fan Yin, Huibin, Hu, Neil Tenenholtz, Lorin Crawford, Lester Mackey, Allison Koenecke
Heterogeneous Treatment Effect Early Stopping Causal Machine Randomized Experiment Heterogeneous Population

June 6, 2023

Fair and Robust Estimation of Heterogeneous Treatment Effects for Policy Learning
Kwangho Kim, José R. Zubizarreta
Optimal Policy Fairness Constraint Robust Estimation Heterogeneous Treatment Effect

April 20, 2023

B-Learner: Quasi-Oracle Bounds on Heterogeneous Causal Effects Under Hidden Confounding
Miruna Oprescu, Jacob Dorn, Marah Ghoummaid, Andrew Jesson, Nathan Kallus, Uri Shalit
Treatment Effect Causal Effect Estimation Heterogeneous Treatment Effect Conditional Average Treatment Effect Hidden Knowledge Latent Confounding B Learner Oracle Inequality

March 30, 2023

Practical Policy Optimization with Personalized Experimentation
Mia Garrard, Hanson Wang, Ben Letham, Shaun Singh, Abbas Kazerouni, Sarah Tan, Zehui Wang, Yin Huang, Yichun Hu, Chad Zhou, Norm Zhou, Eytan Bakshy
Reinforcement Learning Heterogeneous Treatment Effect Experimentation Platform Personalized Experimentation

February 27, 2023

Causal isotonic calibration for heterogeneous treatment effects
Lars van der Laan, Ernesto Ulloa-Pérez, Marco Carone, Alex Luedtke
Heterogeneous Treatment Effect Causal Event Cross Fold Model Prediction Average Isotonic Recalibration

February 24, 2023

Recovering Sparse and Interpretable Subgroups with Heterogeneous Treatment Effects with Censored Time-to-Event Outcomes
Chirag Nagpal, Vedant Sanil, Artur Dubrawski
Many Sparse Treatment Effect Heterogeneous Treatment Effect Structured Sparsity Censored Time to Event Subgroup Description

February 23, 2023

Understanding the Impact of Competing Events on Heterogeneous Treatment Effect Estimation from Time-to-Event Data
Alicia Curth, Mihaela van der Schaar
Global Impact Causal Effect Treatment Effect Event Description Covariate Shift Heterogeneous Treatment Effect Heterogeneous Treatment Effect Estimation Time to Event Data

February 2, 2023

Augmented Learning of Heterogeneous Treatment Effects via Gradient Boosting Trees
Heng Chen, Michael L. LeBlanc, James Y. Dai
High Dimensional Heterogeneous Treatment Effect Gradient Boosted Tree Additive Mixed Model

January 29, 2023

On Heterogeneous Treatment Effects in Heterogeneous Causal Graphs
Richard A Watson, Hengrui Cai, Xinming An, Samuel McLean, Rui Song
Causal Pattern Heterogeneous Treatment Effect Causal Graphical Model Heterogeneous Causal

January 26, 2023

Proximal Causal Learning of Conditional Average Treatment Effects
Erik Sverdrup, Yifan Cui
Loss Function Heterogeneous Treatment Effect Conditional Average Treatment Effect B Learner Proximal Causal

Heterogeneous Treatment Effect

Papers

Combining T-learning and DR-learning: a framework for oracle-efficient estimation of causal contrasts

Causal Machine Learning for Cost-Effective Allocation of Development Aid

Identification and Estimation of Conditional Average Partial Causal Effects via Instrumental Variable

Inferring Heterogeneous Treatment Effects of Crashes on Highway Traffic: A Doubly Robust Causal Machine Learning Approach

Uncertainty Quantification in Heterogeneous Treatment Effect Estimation with Gaussian-Process-Based Partially Linear Model

Business Policy Experiments using Fractional Factorial Designs: Consumer Retention on DoorDash

Personalized Assignment to One of Many Treatment Arms via Regularized and Clustered Joint Assignment Forests

Machine Learning Who to Nudge: Causal vs Predictive Targeting in a Field Experiment on Student Financial Aid Renewal

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

Targeting relative risk heterogeneity with causal forests

Should I Stop or Should I Go: Early Stopping with Heterogeneous Populations

Fair and Robust Estimation of Heterogeneous Treatment Effects for Policy Learning

B-Learner: Quasi-Oracle Bounds on Heterogeneous Causal Effects Under Hidden Confounding

Practical Policy Optimization with Personalized Experimentation

Causal isotonic calibration for heterogeneous treatment effects

Recovering Sparse and Interpretable Subgroups with Heterogeneous Treatment Effects with Censored Time-to-Event Outcomes

Understanding the Impact of Competing Events on Heterogeneous Treatment Effect Estimation from Time-to-Event Data

Augmented Learning of Heterogeneous Treatment Effects via Gradient Boosting Trees

On Heterogeneous Treatment Effects in Heterogeneous Causal Graphs

Proximal Causal Learning of Conditional Average Treatment Effects