Wrong Direction

_{Jan 01 . 1970}

The relation between cause and effect is reversed.

Examples

Diet soda causes weight gain, or is it the other way around?

The Framingham analysis included 9,000 middle-aged men and women followed for four years. Researchers found that compared to people who didn’t drink sodas at all, those who drank both sugar-sweetened and diet soda were more likely to develop metabolic syndrome — a cluster of symptoms often linked to obesity that increase risk for heart disease and diabetes. Because both of these studies were observational, it is impossible to say if the diet sodas played a direct role in the weight gain. It may be that people switch to diet soda when they begin gaining weight without addressing other aspects of their diet that are causing the weight gain.
Salynn Boyles, “Diet Sodas Cause Weight Gain? Not so Fast“, WebMD (November 29, 2010), emphasis added.

The increase in AIDS was caused by more sex education. (In fact, the increase in sex education was caused by the spread of AIDS.)

Critique

Give a causal argument showing that the relation between cause and effect has been reversed.

Commentary

On addressing the symptom instead of the disease…

Fallacy of mistaking Sign for Cause. The quack doctor falls into this, when on seeing certain spots on the body he attacks and removes them, thereby, it may be, sending the malady farther into the frame, instead of curing it in its seat. The quack statesman is guilty of the same error, when discovering the existence of ignorance and crime in a country he contents himself with punishing them, instead of trying to remove the deep moral causes from which they spring. Buckle has, as it appears to us, fallen into the fallacy; he traces all civilization to mere intellectual power, excluding moral causes: whereas the intellect in may cases, as in Scotland and the United States, was awakened by moral causes of which the intellectual was, properly speaking, the effect.
McCosh, The Laws of Discursive Thought, p. 194.

On scurrilous correlations

Linear regression is a marvelous tool, versatile, scalable, and as easy to execute as slicing a button on your spreadsheet. You can use it for data sets involving two variables, like the ones I’ve drawn here, but it works just as well for three variables