Thread by @frod_san

ICYMI this paper really is a must read:

Comparing and choosing best models based on AIC etc, then interpret coefficients causally (what's the effect of X on Y?) is flawed, yet so common.

We must draw causal assumptions first (eg. DAG)

1/7 https://t.co/icU4g8ipNJdoi.org/10.1111/ele.14…

"Model selection is not a valid method for inferring causal relationships.

Model selection is appropriate for predictive inference (i.e. which model best predicts Y?), which is fundamentally distinct from causal inference (i.e. what is the effect of X on Y?)"

Imagine we want to assess the effect of 'Forestry' on 'Species Y'. But we know other things may also affect Y

We could put all these variables in a regression model (what @rlmcelreath calls a causal salad), or build models w/ different subsets of predictors and compare them.

That will lead to biased estimates. The best model based on AIC & BIC includes more predictors and gives biased estimate of Forestry effect on Y

The causal model (based on DAG) has much larger AIC but gives correct estimate

This applies to Machine Learning too (random forests etc). Showing high variable importance does not mean those predictors are important from a causal point of view, only that they are useful to get good predictions

Causal inference is rarely taught, yet seems so important. Many papers do not aim to predict but to make inferences about how important different variables are. It seems we're too often using a wrong approach

I'm trying to learn more about this. This is another recent paper from @ArifSuchinta next on my reading list: #ecopubs

FINdoi.org/10.1002/ecm.15…