Suppose you are given a set of regression results on the role of a random treatment assignment experiment on the effect of an ad campaign on consumers' willingness to buy your product. The regression results report an R-squared of 0.04. How should you value the regression results?
A) Not highly, the R-squared is far too low.
B) Highly, despite the low R-squared it is likely the effect estimated is causal and valuable for active prediction.
C) Highly, the R-squared value is still high enough to justify that the effect is causal.
D) Not highly, the implied correlation of the R-squared is too low.

Question

Quizplus · Accepted Answer

The R-squared value in a regression analysis explains the proportion of the variance for a dependent variable that's explained by an independent variable or variables in a regression model. In the context of a randomized treatment assignment experiment, the primary interest is in estimating the causal effect of the treatment (in this case, an ad campaign) on the outcome (consumers' willingness to buy). The low R-squared value does not diminish the value of the causal inference drawn from the experiment. The causal effect is valuable for making predictions about the impact of the treatment, regardless of the proportion of variance explained in the outcome variable.

Suppose You Are Given a Set of Regression Results on the Role