Define the causal-effect coefficient.

Define the causal-effect coefficient.

• A. The sample size required for causation
• B. The significance level of a test
• C. The p-value of the causal test
• D. The value of causation determined by the measure of association

Answer: (D)

The main concept here is that the causal-effect coefficient represents the numeric magnitude of the causal effect, as quantified by a measure of association. In practice, you estimate how much the outcome changes with exposure using a measure of association such as a risk ratio, odds ratio, risk difference, or a regression-derived coefficient. The causal-effect coefficient is the concrete number produced by that measure, expressing the size of the effect—for example, a risk ratio of 2.0 means the outcome is twice as likely with exposure, or a risk difference of 0.08 means an 8 percentage-point increase. This concept isn’t about how large a study needs to be, nor about the statistical significance level or p-value. Those relate to study design, precision, or evidence against a null hypothesis, not the actual size of the causal effect.

The main concept here is that the causal-effect coefficient represents the numeric magnitude of the causal effect, as quantified by a measure of association. In practice, you estimate how much the outcome changes with exposure using a measure of association such as a risk ratio, odds ratio, risk difference, or a regression-derived coefficient. The causal-effect coefficient is the concrete number produced by that measure, expressing the size of the effect—for example, a risk ratio of 2.0 means the outcome is twice as likely with exposure, or a risk difference of 0.08 means an 8 percentage-point increase.

This concept isn’t about how large a study needs to be, nor about the statistical significance level or p-value. Those relate to study design, precision, or evidence against a null hypothesis, not the actual size of the causal effect.