Causal Inference in Epidemiology

Questions and Answers

What is the primary goal of causal inference?

To measure the risk of an outcome in a population

To identify associations between variables

To distinguish between modifiable and non-modifiable risk factors

To determine the extent to which a cause affects an effect (correct)

What does an association between two variables imply?

That the variables are not related in any way

That knowing something about one variable gives information about the other (correct)

That one variable causes the other

That the variables are independent

What is the key difference between correlation and causation?

Correlation is a type of causation, while causation is a type of correlation

Correlation implies causation, while causation does not imply correlation

Correlation does not imply causation, while causation implies correlation (correct)

Correlation and causation are interchangeable terms

Which of the following is an example of a modifiable risk factor?

Smoking habits Signup and view all the answers

What is the term for two variables that provide no information about each other?

Independent variables Signup and view all the answers

What is a risk factor?

A factor that is correlated with an increased chance of an outcome. Signup and view all the answers

What defines a population at risk?

Individuals unaffected at the start of the study who may experience the risk event. Signup and view all the answers

What is required for causal inference regarding a risk factor?

Exposure to the risk factor being studied is essential. Signup and view all the answers

How is risk quantitatively defined?

As the probability that an event will occur. Signup and view all the answers

In a smoking and lung cancer study, what is the main conclusion drawn?

An association exists between smoking and lung cancer among the exposed population. Signup and view all the answers

What can be inferred about risk factors in a study?

Identifying risk factors helps in understanding and predicting outcomes. Signup and view all the answers

Study Notes

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.