Causal Inference in Epidemiology

Causal Inference Overview

• Causal inference investigates whether a relationship between two variables is causal or merely an association.
• Essential questions: Did an event result from an action (falling vs. being pushed)?
• The primary focus is on understanding the cause-effect relationship and the extent of such effects.

Key Concepts

• Modifiable Risk Factors: Factors that can be changed to reduce risk (e.g., lifestyle choices, environmental exposures).
• Association: Occurs when knowing about one variable provides information about another; does not imply causation.
• Correlation: A statistical measure that indicates the extent to which two variables fluctuate together.

Types of Risk Factors

• Various classifications of risk factors exist, crucial for understanding public health and epidemiology.
• Risk factors can be modifiable (e.g., smoking, diet) or non-modifiable (e.g., genetics, age).

Measures of Risk in Populations

• Understanding how to quantify risk is vital in epidemiology to identify the impact of risk factors on populations.
• Different statistical measures, such as relative risk and odds ratios, provide insights into the strength of associations and potential causal relationships.

Independence of Variables

• Two variables are independent if knowledge about one provides no predictive power regarding the other.
• Establishing independence is crucial in distinguishing between mere correlation and true causation.

Causal Inference

• Includes three key concepts: Association, Correlation, and Causation.

Risk Factors

• Defined as elements associated with a higher likelihood of a specific outcome.
• Examples include factors that increase the risk of developing a condition (e.g., condition X).
• Risk is quantified as the probability of an event occurring.

Risk Population

• Consists of individuals who are initially unaffected by a risk factor at the start of a study.
• These individuals experience the outcome of interest (e.g., disease, death) during the study period.

Exposure

• Essential for causal inference; subjects must be exposed to the risk factor under investigation.
• An example is a study assessing the link between smoking and lung cancer, which uses a sample of adults not previously exposed to cigarette smoke.

Key Considerations

• The absence of a control group in studies can complicate conclusions about causation.
• Establishing a direct association requires careful interpretation of data and potential confounding factors.

Learn about the concepts of modifiable risk factors, association, and causation. Explore different types of risk factors and measures of risk in populations.