Explain in layperson's terms why predictive models aren't causally interpretable Imagine that you are asked to infer some causal effect -- a change in an outcome $y$ in response to some variable $x$.  But, the person asking for this directs you to use a predictive model to do so.  Here's the setup:

*

*$x$ is confounded inasmuch as there is some unobserved $u$ that is causally linked to both $y$ and $x$.  We have a classical omitted variables bias.

*We have high dimensional covariates $\mathbf{Z}$ that are not independent of $y$ or $x$ and/or $u$

*You are asked to train a suite of predictive models -- neural networks, boosted trees, whatever -- denoted $g_i([x, \mathbf{Z}]) + \epsilon$ where $i$ indexes different models, and then select among them model $i$ that minimizes some metric of predictive skill.  RMSE, for instance.

*Based on the chosen model, you are asked to report
$$
\frac{\partial \hat{y}}{\partial x} = \frac{\partial \hat{g}_i([x, \mathbf{Z}])}{\partial x}
$$

*You know that
$$
E\left[\frac{\partial \hat{y}}{\partial x}\right] \neq \frac{\partial y}{\partial x}
$$
in the population, because the error term includes the omitted variable, so therefore
$$
\frac{\partial \epsilon}{\partial x} \neq 0 \text{  in the population, despite the fact that } \frac{\partial \hat\epsilon}{\partial x} = 0
$$
in any reasonable model $g$.

On top of omitted variables bias, there may be bias from regularization too!

*

*Further assume that you have some causal model -- say an instrumental variables regression, utilizing some suitable instrument $w$ for $x$.  It's one of the models in your suite of models, but its predictive skill in terms of cross-validated RMSE is worse than the  others.

The best model is the one that produces the consistent causal estimate, right?  But:
How would you explain this to someone in layperson's terms?
The person asking for analysis doesn't understand causal inference, and needs to be educated.  However, they don't understand math and have little attention span.  How can you effectively convey the basic point that causal methods are required, and predictive methods are inappropriate?  No math, lots of stories, pithy sentences.

If it's a person who really knows barely anything about statistics and causation I would provide some examples that are straightforward.
If you have data on the number of bathrooms in someone's largest house, you can classify the person as a billionaire or not, in the sense that billionaire mansions have numerous bathrooms. I'd assume you could achieve some precise estimates in this problem. However, increasing the number of bathrooms in someone's house won't make them billionaires. So you could use such a model to identify billionaires, but not to make someone a billionaire. Correlation, but not causation.
Another common example is the ice cream consumption relationship to shark attacks (being confounded by temperature). In some places, you could get some reasonable prediction of shark attacks based on ice cream consumption. But throwing more sharks in beaches wouldn't make people eat more ice cream (at least not to the same extent you measured with the confounded estimate).
I agree with Graham Wright that this should not be a dichotomy. You can have predictions based on causal models, for example. If the person knows some statistics, I would go for a different approach, like the other answers, but if it's my not-even-high-school-diploma grandma, I would go for the intuitive examples I gave above.
