Distinguishing between mediators and confounders is important for the causal inference in observational studies
Zhongheng Zhang
Abstract
Zhongheng Zhang
Abstract
Observational studies are common in clinical literature because they are less costly and easy to perform, as compared to the randomized controlled trials (RCT). The limitation of such kind of studies is their inability to draw a causal inference due to potential bias. However, there are many approaches being proposed in recent years for the causal inference in observational studies (1-3). Although these methods cannot replace RCTs for causal inference, analysis in their framework can help to make the causal evidence more reliable. In the following discuss, I would like to discuss some of these points by using a published article.
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Observational studies are common in clinical literature because they are less costly and easy to perform, as compared to the randomized controlled trials (RCT). The limitation of such kind of studies is their inability to draw a causal inference due to potential bias. However, there are many approaches being proposed in recent years for the causal inference in observational studies (1-3). Although these methods cannot replace RCTs for causal inference, analysis in their framework can help to make the causal evidence more reliable. In the following discuss, I would like to discuss some of these points by using a published article.
Key concepts: Observational study, Causal inference, Confounding, Randomized controlled trial, Inference, Medicine, Marginal structural model, Computer science