In linear causal models the total effect of an action is equal to the direct effect of that action and its indirect effect. We can think of an oversimplified anti-tobacco public health campaign to conceptualize this equation. A campaign could be developed to use famous celebrities in advertisements against smoking. This approach may have a direct effect on teen smoking rates if teens see the advertisements and decide not to smoke as a result of the influential messaging from their favorite celebrity. This approach may also have indirect effects. Imagine a teen who didn’t see the advertising, but their best friend did see it. If their best friend was influenced, then they may adopt their friend’s anti-smoking stance. This would be an indirect effect of the advertising campaign in the positive direction. The total effect of the campaign would then be the kids who were directly deterred from smoking combined with those who didn’t smoke because their friends were deterred.
However, linear causal models don’t capture all of the complexity that can exist within causal models. As Judea Pearl explains in The book of Why, there can be complex causal models where the equation that I started this post with doesn’t hold. Pearl uses a drug used to treat a disease as an example of a situation where the direct effect and indirect effect of a drug don’t equal the total effect. He says that in situations where a drug causes the body to release an enzyme that then combines with the drug to treat a disease, we have to think beyond the equation above. In this case he writes, “the total effect is positive but the direct and indirect effects are zero.”
The drug itself doesn’t do anything to combat the disease. It stimulates the release of an enzyme and without that enzyme the drug is ineffective against the disease. The enzyme also doesn’t have a direct effect on the disease. The enzyme is only useful when combined with the drug, so there is no indirect effect that can be measured as a result of the original drug being introduced. The effect is mediated between the interaction of both the drug and enzyme together. In the model Pearl shows us, there is only the mediating effect, not a direct or indirect effect.
This model helps us see just how complicated ideas and conceptions of causation are. Most of the time we think about direct effects, and we don’t always get to thinking about indirect effects combined with direct effects. Good scientific studies are able to capture the direct and indirect effects, but to truly understand causation today, we have to be able to include mediating effects in complex causation models like the one Pearl describes.
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