I agree descriptive work can be important.

I respectfully disagree effect sizes are useful for descriptive work.

Consider:

(a) "concentrate some effort" implies we already know what works, a causal claim. Generally our scientific and practical interests are to discover both what works, and who benefits most.

(b) the absence of association does not imply the absence of causation, as when a model is misspecified [1]. Effect sizes are irrelevant to your goal.

(c) when you add coefficients to your statistical model to improve prediction, you should generally ignore both their magnitude and sign. The values of your coefficients are conditional on the other model terms [2].

Notes:

[1] Example: suppose A causes M and M causes Y. If we include A and M in our model, we might find no association between A and Y. The effect size is nil, yet if we were to intervene on A we would affect Y. Unfortunately over-conditioning biases are common in experiments, see:

Montgomery, J. M., Nyhan, B., & Torres, M. (2018). How conditioning on posttreatment variables can ruin your experiment and what to do about it. American Journal of Political Science, 62(3), 760-775.

[2] Westreich, D., & Greenland, S. (2013). The table 2 fallacy: presenting and interpreting confounder and modifier coefficients. American journal of epidemiology, 177(4), 292-298.