Flexible Machine Learning Estimation of Conditional Average Treatment Effects: A Blessing and a Curse

Clinical Pharmacogenetics Implementation Consortium Guideline for CYP2C19 Genotype and Clopidogrel Therapy: 2022 Update

(2022) Craig R. Lee et al.   CLINICAL PHARMACOLOGY & THERAPEUTICS

Predicting counterfactual risks under hypothetical treatment strategies: an application to HIV

(2022) Barbra A. Dickerman et al.   EUROPEAN JOURNAL OF EPIDEMIOLOGY

Estimating individual treatment effects using non‐parametric regression models: A review

(2022) Alberto Caron et al.   JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES A-STATISTICS IN SOCIETY

13 Questions About Using Machine Learning in Causal Research (You Won’t Believe the Answer to Number 10!)

(2021) Stephen J Mooney et al.   AMERICAN JOURNAL OF EPIDEMIOLOGY

Machine Learning in Causal Inference: How do I love thee? Let me count the ways.

(2021) Laura B Balzer et al.   AMERICAN JOURNAL OF EPIDEMIOLOGY

Demystifying Statistical Inference When Using Machine Learning in Causal Research

(2021) Laura B Balzer et al.   AMERICAN JOURNAL OF EPIDEMIOLOGY

Learning and confirming a class of treatment responders in clinical trials

(2021) Victor B. Talisa et al.   STATISTICS IN MEDICINE

From real‐world patient data to individualized treatment effects using machine learning: Current and future methods to address underlying challenges

(2020) Ioana Bica et al.   CLINICAL PHARMACOLOGY & THERAPEUTICS

Prediction meets causal inference: the role of treatment in clinical prediction models

(2020) Nan van Geloven et al.   EUROPEAN JOURNAL OF EPIDEMIOLOGY

Machine Learning Estimation of Heterogeneous Causal Effects: Empirical Monte Carlo Evidence

(2020) Michael C Knaus et al.   Econometrics Journal

Counterfactual prediction is not only for causal inference

(2020) Barbra A. Dickerman et al.   EUROPEAN JOURNAL OF EPIDEMIOLOGY

The association of non‐alcoholic fatty liver disease and cardiac structure and function—Framingham Heart Study

(2020) Laura S. Chiu et al.   LIVER INTERNATIONAL

Assessing Heterogeneity of Treatment E↵ects in Observational Studies

(2020) Sarah E Robertson et al.   AMERICAN JOURNAL OF EPIDEMIOLOGY

Quasi-Oracle Estimation of Heterogeneous Treatment Effects

(2020) X Nie et al.   BIOMETRIKA

Estimation of Conditional Average Treatment Effects With High-Dimensional Data

(2020) Qingliang Fan et al.   JOURNAL OF BUSINESS & ECONOMIC STATISTICS

Precision Medicine

(2019) Michael R. Kosorok et al.   Annual Review of Statistics and Its Application

Metalearners for estimating heterogeneous treatment effects using machine learning

(2019) Sören R. Künzel et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

Generalized random forests

(2019) Susan Athey et al.   ANNALS OF STATISTICS

Reflection on modern methods: when worlds collide—prediction, machine learning and causal inference

(2019) Tony Blakely et al.   INTERNATIONAL JOURNAL OF EPIDEMIOLOGY

Big Data in Public Health: Terminology, Machine Learning, and Privacy

(2018) Stephen J. Mooney et al.   Annual Review of Public Health

Double/debiased machine learning for treatment and structural parameters

(2018) Victor Chernozhukov et al.   Econometrics Journal

Estimating Individual Treatment Effect in Observational Data Using Random Forest Methods

(2018) Min Lu et al.   JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS

Estimation and Inference of Heterogeneous Treatment Effects using Random Forests

(2018) Stefan Wager et al.   JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION

Some methods for heterogeneous treatment effect estimation in high dimensions

(2018) Scott Powers et al.   STATISTICS IN MEDICINE

Comparing methods for estimation of heterogeneous treatment effects using observational data from health care databases

(2018) T. Wendling et al.   STATISTICS IN MEDICINE

Targeted Maximum Likelihood Estimation for Causal Inference in Observational Studies

(2016) Megan S. Schuler et al.   AMERICAN JOURNAL OF EPIDEMIOLOGY

Recursive partitioning for heterogeneous causal effects: Table 1.

(2016) Susan Athey et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

Estimating treatment effect heterogeneity in randomized program evaluation

(2013) Kosuke Imai et al.   Annals of Applied Statistics

The Framingham Heart Study and the epidemiology of cardiovascular disease: a historical perspective

(2013) Syed S Mahmood et al.   LANCET

Stochastic counterfactuals and stochastic sufficient causes

(2011) Tyler J. Vanderweele et al.   STATISTICA SINICA

Subgroup identification from randomized clinical trial data

(2011) Jared C. Foster et al.   STATISTICS IN MEDICINE

On the Distinction Between Interaction and Effect Modification

(2010) Tyler J. VanderWeele   EPIDEMIOLOGY

Bayesian Nonparametric Modeling for Causal Inference

(2010) Jennifer L. Hill   JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS

The Consistency Statement in Causal Inference

(2008) Stephen R. Cole et al.   EPIDEMIOLOGY