Causality

TL;DR

• Causality denotes a cause-and-effect relationship between two variables, not merely correlation.
• Establishing causality enables prediction and evaluation of interventions on outcomes.
• Common approaches to establish causality include experimental designs, statistical techniques, and causal modeling.

Definition

Causality in statistics refers to the relationship between two variables, where one variable (the cause) has an effect on the other variable (the outcome). It is the concept that the relationship between two variables is not simply a correlation, but that one variable directly affects the other.

Explanation

Causality is a fundamental concept in statistics because it allows researchers to establish cause-and-effect relationships between variables. Establishing causality is essential for making predictions and determining the effect of interventions on a specific outcome.

There are several ways to establish causality in statistics:

• Experimental designs: Researchers randomly assign participants to different conditions or treatments and measure the outcome of interest to infer cause-and-effect.
• Statistical techniques: Methods such as regression analysis examine relationships among multiple variables to identify which variables most strongly impact an outcome.
• Causal modeling: Mathematical models represent relationships between variables and test the effect of one variable on another.

Examples

Experimental design example

In a study on the effects of a new medication on blood pressure, researchers could randomly assign participants to receive either the medication or a placebo and then measure their blood pressure levels. By comparing the blood pressure levels between the two groups, researchers can establish a cause-and-effect relationship between the medication and blood pressure.

Statistical technique (regression) example

In a study on the factors that influence a person’s risk of developing heart disease, researchers could use regression analysis to identify the variables that are most strongly associated with heart disease, such as age, gender, and lifestyle factors.

Causal modeling example

In a study on the relationship between air pollution and respiratory illness, researchers could use causal modeling to identify the specific components of air pollution that are most strongly associated with respiratory illness and to determine the size of the effect of air pollution on respiratory illness.

Related terms

• Experimental design
• Regression analysis
• Causal modeling