Add to ORKGIn observational studies evaluating the treatment effect on a given outcome, the treated and untreated subjects may be highly unbalanced in their observed covariates, and these differences can lead to biased estimates of treatment effects. Propensity score is popular tool to reduce this bias. In this work we propose to estimate the propensity score by using Bayesian Networks as alternative to conventional logistic regression. Based on it, we develop an inferential methodology to evaluate the treatment effect. In simulation study, our proposed approach resulted in the best performance
Confounding can cause substantial bias in nonexperimental studies that aim to estimate causal effect...
McCandless, Gustafson and Austin (2009) describe a Bayesian approach to regression adjustment for th...
Propensity score matching and inverse-probability weighting are popular methods for causal inference...
In observational studies evaluating the treatment effect on a given outcome, the treated and untreat...
Propensity scores analysis (PSA) involves regression adjustment for the estimated propensity scores,...
SUMMARY In the analysis of observational data, stratifying patients on the estimated propensity scor...
Randomization of treatment assignment in experiments generates treatment groups with approximately b...
Regression adjustment for the propensity score is a statistical method that reduces confoundingfrom ...
Propensity scores are commonly employed in observational study settings where the goal is to estimat...
The assessment of treatment effects from observational studies may be biased with patients not rando...
The principal aim of analysis of any sample of data is to draw causal inferences about the effects o...
In this article we develop the theoretical properties of the propensity function, which is a general...
The propensity score analysis is a statistical tool for deriving causal inferences in a broad class ...
Propensity score methods are increasingly being used to reduce or minimize the effects of confoundin...
In this article we develop the theoretical properties of the propensity function, which is a general...
Confounding can cause substantial bias in nonexperimental studies that aim to estimate causal effect...
McCandless, Gustafson and Austin (2009) describe a Bayesian approach to regression adjustment for th...
Propensity score matching and inverse-probability weighting are popular methods for causal inference...
In observational studies evaluating the treatment effect on a given outcome, the treated and untreat...
Propensity scores analysis (PSA) involves regression adjustment for the estimated propensity scores,...
SUMMARY In the analysis of observational data, stratifying patients on the estimated propensity scor...
Randomization of treatment assignment in experiments generates treatment groups with approximately b...
Regression adjustment for the propensity score is a statistical method that reduces confoundingfrom ...
Propensity scores are commonly employed in observational study settings where the goal is to estimat...
The assessment of treatment effects from observational studies may be biased with patients not rando...
The principal aim of analysis of any sample of data is to draw causal inferences about the effects o...
In this article we develop the theoretical properties of the propensity function, which is a general...
The propensity score analysis is a statistical tool for deriving causal inferences in a broad class ...
Propensity score methods are increasingly being used to reduce or minimize the effects of confoundin...
In this article we develop the theoretical properties of the propensity function, which is a general...
Confounding can cause substantial bias in nonexperimental studies that aim to estimate causal effect...
McCandless, Gustafson and Austin (2009) describe a Bayesian approach to regression adjustment for th...
Propensity score matching and inverse-probability weighting are popular methods for causal inference...