Conditional Average Treatment Effect

Conditional Average Treatment Effect (CATE) estimation aims to determine the average treatment effect for specific subgroups defined by their characteristics, moving beyond overall average treatment effects. Current research focuses on improving CATE estimation accuracy and robustness using various machine learning models, including Bayesian Additive Regression Trees (BART), doubly robust methods, and neural networks (e.g., G-Transformers, CrossNet), often addressing challenges like high-dimensionality, non-linearity, and unobserved confounding. These advancements are crucial for personalized interventions in diverse fields such as medicine, policy, and marketing, enabling more targeted and effective treatments or strategies based on individual characteristics.

Papers

March 31, 2024

C-XGBoost: A tree boosting model for causal effect estimation
Niki Kiriakidou, Ioannis E. Livieris, Christos Diou
Full Model Causal Inference Causal Effect Estimation Conditional Average Treatment Effect

March 5, 2024

Triple/Debiased Lasso for Statistical Inference of Conditional Average Treatment Effects
Masahiro Kato
High Dimensional Causal Effect Conditional Average Treatment Effect Statistical Inference LASSO Regression

February 28, 2024

Unveiling the Potential of Robustness in Selecting Conditional Average Treatment Effect Estimators
Yiyan Huang, Cheuk Hang Leung, Siyi Wang, Yijun Li, Qi Wu
Native Robustness Full Potential Conditional Average Treatment Effect

February 5, 2024

Estimation of conditional average treatment effects on distributed confidential data
Yuji Kawamata, Ryoki Motai, Yukihiko Okada, Akira Imakura, Tetsuya Sakurai
Raw Data Estimation Task Privacy Preserving State Estimation Conditional Average Treatment Effect Double Machine Learning Data Collaboration

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 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 16, 2024

Privacy Preserving Adaptive Experiment Design
Jiachun Li, Kaining Shi, David Simchi-Levi
Contextual Bandit Conditional Average Treatment Effect Adaptive Experiment Treatment Assignment Adaptive Differential Privacy

December 24, 2023

The Challenge of Using LLMs to Simulate Human Behavior: A Causal Inference Perspective
George Gui, Olivier Toubia
Large Language Model Causal Inference Challenge Task Human Behavior Conditional Average Treatment Effect Global Endogenous Variate LLM Simulation

November 19, 2023

Bounds on Representation-Induced Confounding Bias for Treatment Effect Estimation
Valentyn Melnychuk, Dennis Frauen, Stefan Feuerriegel
Treatment Effect Conditional Average Treatment Effect General Bound Low Dimensional Representation Biased Causal Effect

November 7, 2023

Counterfactual Data Augmentation with Contrastive Learning
Ahmed Aloui, Juncheng Dong, Cat P. Le, Vahid Tarokh
Contrastive Learning Imputation Algorithm Conditional Average Treatment Effect Imputation Accuracy Counterfactual Data Augmentation

October 27, 2023

Optimal Transport for Treatment Effect Estimation
Hao Wang, Zhichao Chen, Jiajun Fan, Haoxuan Li, Tianqiao Liu, Weiming Liu, Quanyu Dai, Yichao Wang, Zhenhua Dong, Ruiming Tang
Optimal Transport Treatment Effect Conditional Average Treatment Effect Unobserved Confounders Counterfactual Prediction Treatment Selection Bias

October 25, 2023

August 28, 2023

Conformal Meta-learners for Predictive Inference of Individual Treatment Effects
Ahmed Alaa, Zaid Ahmad, Mark van der Laan
Conditional Average Treatment Effect Conformal Inference Predictive Inference Individual Treatment Effect Pseudo Likelihood Conformal Meta Learner

May 19, 2023

Meta-learning for heterogeneous treatment effect estimation with closed-form solvers
Tomoharu Iwata, Yoichi Chikahara
Meta Learning Conditional Average Treatment Effect Heterogeneous Treatment Effect Estimation

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

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

January 20, 2023

Causal Inference under Data Restrictions
Xiaoqing Tan
Causal Inference Conditional Average Treatment Effect Individualized Decision

November 19, 2022

BENK: The Beran Estimator with Neural Kernels for Estimating the Heterogeneous Treatment Effect
Stanislav R. Kirpichenko, Lev V. Utkin, Andrei V. Konstantinov
Linear Estimator Heterogeneous Treatment Effect Conditional Average Treatment Effect Neural Kernel Survival Function Random Survival Forest

November 14, 2022

Partial counterfactual identification and uplift modeling: theoretical results and real-world assessment
Théo Verhelst, Denis Mercier, Jeevan Shrestha, Gianluca Bontempi
Theoretical Analysis High Quality Counterfactuals Conditional Independence Conditional Average Treatment Effect Counterfactual State

Conditional Average Treatment Effect

Papers

C-XGBoost: A tree boosting model for causal effect estimation

Triple/Debiased Lasso for Statistical Inference of Conditional Average Treatment Effects

Unveiling the Potential of Robustness in Selecting Conditional Average Treatment Effect Estimators

Estimation of conditional average treatment effects on distributed confidential data

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

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

Privacy Preserving Adaptive Experiment Design

The Challenge of Using LLMs to Simulate Human Behavior: A Causal Inference Perspective

Bounds on Representation-Induced Confounding Bias for Treatment Effect Estimation

Counterfactual Data Augmentation with Contrastive Learning

Optimal Transport for Treatment Effect Estimation

Causal Q-Aggregation for CATE Model Selection

CATE Lasso: Conditional Average Treatment Effect Estimation with High-Dimensional Linear Regression

Conformal Meta-learners for Predictive Inference of Individual Treatment Effects

Meta-learning for heterogeneous treatment effect estimation with closed-form solvers

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

Proximal Causal Learning of Conditional Average Treatment Effects

Causal Inference under Data Restrictions

BENK: The Beran Estimator with Neural Kernels for Estimating the Heterogeneous Treatment Effect

Partial counterfactual identification and uplift modeling: theoretical results and real-world assessment