Bayesian causal inference: a critical review
This paper provides a critical review of the Bayesian perspective of causal inference based
on the potential outcomes framework. We review the causal estimands, assignment …
on the potential outcomes framework. We review the causal estimands, assignment …
Explainable machine learning can outperform Cox regression predictions and provide insights in breast cancer survival
A Moncada-Torres, MC van Maaren, MP Hendriks… - Scientific reports, 2021 - nature.com
Abstract Cox Proportional Hazards (CPH) analysis is the standard for survival analysis in
oncology. Recently, several machine learning (ML) techniques have been adapted for this …
oncology. Recently, several machine learning (ML) techniques have been adapted for this …
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 …
Practical guide to honest causal forests for identifying heterogeneous treatment effects
Abstract “Heterogeneous treatment effects” is a term which refers to conditional average
treatment effects (ie, CATEs) that vary across population subgroups. Epidemiologists are …
treatment effects (ie, CATEs) that vary across population subgroups. Epidemiologists are …
Survite: Learning heterogeneous treatment effects from time-to-event data
We study the problem of inferring heterogeneous treatment effects from time-to-event data.
While both the related problems of (i) estimating treatment effects for binary or continuous …
While both the related problems of (i) estimating treatment effects for binary or continuous …
Mutual information-based neighbor selection method for causal effect estimation
Estimation of causal effects from observational data has been the main objective in several
high-impact scientific domains, while the golden standard for calculating the true causal …
high-impact scientific domains, while the golden standard for calculating the true causal …
Causal Bayesian machine learning to assess treatment effect heterogeneity by dexamethasone dose for patients with COVID-19 and severe hypoxemia
The currently recommended dose of dexamethasone for patients with severe or critical
COVID-19 is 6 mg per day (mg/d) regardless of patient features and variation. However …
COVID-19 is 6 mg per day (mg/d) regardless of patient features and variation. However …
Rule ensemble method with adaptive group lasso for heterogeneous treatment effect estimation
K Wan, K Tanioka, T Shimokawa - Statistics in Medicine, 2023 - Wiley Online Library
The increasing scientific attention given to precision medicine based on real‐world data has
led to many recent studies clarifying the relationships between treatment effects and patient …
led to many recent studies clarifying the relationships between treatment effects and patient …
Variable selection with missing data in both covariates and outcomes: Imputation and machine learning
Variable selection in the presence of both missing covariates and outcomes is an important
statistical research topic. Parametric regression are susceptible to misspecification, and as a …
statistical research topic. Parametric regression are susceptible to misspecification, and as a …
Treatment heterogeneity with survival outcomes
This chapter is accompanied by survlearners, a package that provides well-documented
implementations of the conditional average treatment effects (CATE) estimation strategies …
implementations of the conditional average treatment effects (CATE) estimation strategies …