Categories → Causal Inference , Variables
A frontdoor variable is a mediator variable that blocks the effect of confounding variables on the relationship between a treatment and an outcome.
In causal inference, a frontdoor variable is a variable that mediates the relationship between a treatment or exposure variable and an outcome variable, while also blocking the effect of any confounding variables that may influence the treatment-outcome relationship.
A key benefit of the frontdoor variable is that they provide an alternative method to identify the causal effect when backdoor and Instrumental Variables approaches are not applicable.
An excellent and detailed introduction to the Frontdoor variable and its uses in causal inference can be found in "The Paper of How: Estimating Treatment Effects Using the Front-Door Criterion" by Bellemare and Bloem.
A quick and intuitive explanation of the use of Frontdoor variables for identification can be found in this video by Brady Neal:
https://www.youtube.com/watch?v=-kWocwaXqlk
To illustrate this concept, consider a hypothetical scenario where researchers want to investigate the effect of a new medication on reducing the risk of heart attacks. They gather data on patients who either received the medication or a placebo, and also record their age and smoking status as potential confounding variables that could affect the treatment-outcome relationship.
In this scenario, age and smoking status are potential confounding variables, meaning they are associated with both the treatment (medication) and the outcome (heart attack). However, a frontdoor variable could be a variable that mediates the relationship between the medication and heart attack risk, while also being influenced by age and smoking status.
For example, suppose researchers found that the medication reduced the risk of blood clots, which in turn decreased the risk of heart attacks. Blood clots could be considered a frontdoor variable in this scenario, as they mediate the effect of the medication on heart attack risk, while also being influenced by age and smoking status.
By identifying and adjusting for a frontdoor variable, researchers can potentially isolate the direct effect of the treatment on the outcome, while also controlling for any confounding variables that may influence the treatment-outcome relationship.
In Causal Wizard, frontdoor variables are automatically detected during the Check process.