The aim of this article was to perform a scoping review of methods available for dealing with
confounding when analyzing the effect of health care treatments with single‐point exposure …

Ensemble modelling is a quantitative method that combines information from multiple
individual models and has shown great promise in statistical machine learning. Ensemble …

Estimating a treatment effect from observational data requires modeling treatment and
outcome subject to uncertainty/misspecification. A previous research has shown that it is not …

Causal mediation effect estimates can be obtained from marginal structural models using
inverse probability weighting with appropriate weights. In order to compute weights …

Randomized controlled trials are the gold standard for measuring causal effects. However,
they are often not always feasible, and causal treatment effects must be estimated from …

Observational studies are often used to understand relationships between exposures and
outcomes. They do not, however, allow conclusions about causal relationships to be drawn …

An important goal in causal inference is to achieve balance in the covariates among the
treatment groups. In this article, we introduce the concept of distributional balance …

Background: Randomized controlled trials offer the best design for eliminating bias in
estimating treatment effects but can be slow and costly in rare disease research …

Abstract Propensity Score Matching is a popular approach to evaluate treatment effects in
observational studies. Regrettably, practitioners often overlook the issue of model …

In observational study, the propensity score has the central role to estimate causal effects.
Since the propensity score is usually unknown, estimating by appropriate procedures is an …