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Often, randomization of treatment is unethical. For example, it would be unethical to force hospital patients who don’t like animals to receive therapy animal services. Randomized experiments can also be expensive and demand a lot of resources. It is often more practical to use data that is already available. In these scenarios, the treatment group is not random but is based on other factors, such as personal preference.

When randomization is not possible or plausible, several sources of bias can impact the accuracy of estimated causal effects. One source of bias is selection bias. Selection bias is bias that happens because of how individuals were put into the treatment or control groups.

In terms of our therapy animal example, selection bias might arise if:

  • individuals choose whether they want the therapy (dog-lovers might be over-represented in this case)
  • individuals in the control group come from a different hospital
  • individuals are only able to receive the therapy based on another variable, such as insurance coverage

If any of these variables that are associated with treatment assignment are also related to the outcome variable (cortisol level), they are considered confounders, or confounding variables. These variables may lead us to incorrect conclusions about the impact of the treatment on the outcome.

The following is a graphical representation of how the confounder X is associated with both the treatment Z and outcome Y:

Two diagrams with boxes labeled with letters and arrows connecting the boxes. Diagram 1 shows no confounding because the Z box has an arrow pointing to the Y box. Diagram 2 shows confounding because there is an additional X box with arrows pointing to both the Z and Y boxes.

Instructions

To illustrate the concept of confounding variables, consider the following example. Suppose we are interested in learning about the effect of coffee on blood pressure. If we assume that there are no confounding variables impacting coffee intake or blood pressure, the causal relationship would look something like the first diagram in the learning environment where drinking coffee and high blood pressure are linked by a single arrow.

Assuming that there are no confounders that are related to both coffee intake and blood pressure is unrealistic from a scientific perspective. We know that people who smoke cigarettes tend to drink more coffee than people who do not. We also know that nicotine in cigarettes is associated with higher blood pressure. Therefore, it is reasonable to conclude that cigarette smoking is a potential confounder in the relationship between coffee intake and blood pressure. In our diagram, we add smoking cigarettes as a confounder that points to both drinking coffee and high blood pressure.

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