Causal effect estimation: Recent progress, challenges, and opportunities
Causal inference has numerous real-world applications in many domains, such as health
care, marketing, political science, and online advertising. Treatment effect estimation, a …
care, marketing, political science, and online advertising. Treatment effect estimation, a …
Core concepts in pharmacoepidemiology: Violations of the positivity assumption in the causal analysis of observational data: Consequences and statistical …
Y Zhu, RA Hubbard, J Chubak, J Roy… - … and drug safety, 2021 - Wiley Online Library
In the causal analysis of observational data, the positivity assumption requires that all
treatments of interest be observed in every patient subgroup. Violations of this assumption …
treatments of interest be observed in every patient subgroup. Violations of this assumption …
Bayesian regression tree models for causal inference: Regularization, confounding, and heterogeneous effects (with discussion)
This paper presents a novel nonlinear regression model for estimating heterogeneous
treatment effects, geared specifically towards situations with small effect sizes …
treatment effects, geared specifically towards situations with small effect sizes …
Complete mesocolic excision for right colonic cancer: prospective multicentre study
SR Benz, IS Feder, S Vollmer, Y Tam… - British Journal of …, 2023 - academic.oup.com
Background Complete mesocolic excision (CME) for right colonic cancer is a more complex
operation than standard right hemicolectomy but evidence to support its routine use is still …
operation than standard right hemicolectomy but evidence to support its routine use is still …
Estimating heterogeneous survival treatment effect in observational data using machine learning
Methods for estimating heterogeneous treatment effect in observational data have largely
focused on continuous or binary outcomes, and have been relatively less vetted with …
focused on continuous or binary outcomes, and have been relatively less vetted with …
Using tree-based machine learning for health studies: literature review and case series
Tree-based machine learning methods have gained traction in the statistical and data
science fields. They have been shown to provide better solutions to various research …
science fields. They have been shown to provide better solutions to various research …
Comparative effectiveness of initial treatment for infantile spasms in a contemporary US cohort
ZM Grinspan, KG Knupp, AD Patel, EG Yozawitz… - Neurology, 2021 - AAN Enterprises
Objective To compare the effectiveness of initial treatment for infantile spasms. Methods The
National Infantile Spasms Consortium prospectively followed up children with new-onset …
National Infantile Spasms Consortium prospectively followed up children with new-onset …
Tree‐based machine learning to identify and understand major determinants for stroke at the neighborhood level
Background Stroke is a major cardiovascular disease that causes significant health and
economic burden in the United States. Neighborhood community‐based interventions have …
economic burden in the United States. Neighborhood community‐based interventions have …
Comparison of robotic versus laparoscopic versus open distal gastrectomy for locally advanced gastric cancer: a prospective trial-based economic evaluation
Importance It is largely unclear whether robotic distal gastrectomy (RDG) is cost-effective for
locally advanced gastric cancer (LAGC). Objective To evaluate the cost-effectiveness of …
locally advanced gastric cancer (LAGC). Objective To evaluate the cost-effectiveness of …
Estimating multi-cause treatment effects via single-cause perturbation
Most existing methods for conditional average treatment effect estimation are designed to
estimate the effect of a single cause-only one variable can be intervened on at one time …
estimate the effect of a single cause-only one variable can be intervened on at one time …