Teaching DAGs sounds easy, until students actually have to make one themselves. A new tutorial lays out simple steps to make it clear and easy for students to understand. Draw process, selection, outcome, confounds. From Nathan J. Quimpo & Peter M. Steiner files.osf.io/v1/resources... #psych

Another view on propensity score (PS) in DAGs. academic.oup.com/ije/article-... PS is a mediator like covariates -> PS -> treatment. I think we are obsessed with seeing how conditioning on PS makes the balancing property in DAGs. Maybe d-separation is simply not the right tool. Just accept it.

"No Manipulation, No Causation"? She's crying because of her height. How to intervene on height doesn't matter. Height clearly causes her crying. Maybe studying it isn't useful, but utility is not ontology.

You may very well think that Philosophy is not a fact-based discipline. Bryan Frances will disabuse you of such thoughts, offering 200 philosophical facts in evidence: buff.ly/lDX367Y

Lots to say on this, but one thing recently came to my mind is the isomorphism between “wide format” & “long format” for panel datasets. The latter treats time as a variable, which confuses the issue, at least conceptually. DiD with wide format is clear and here: journals.sagepub.com/doi/10.1177/...

How to draw propensity scores (PS) in DAGs? Some (me also) claim it is like "treatment -> PS <- covariates", since in order to compute PS we need both treatment and covariates. This view has confused me for so long, and now I think I was wrong. My letter here: track.smtpsendmail.com/9032119/c?p=...

What does “unconditional” really mean? P(data) seems unconditional, and P(data | boys) conditional. But imagine an alien landing on Earth and seeing P(data). It says, “Oh, so you’re conditioning on humans, not tigers.” Every “unconditional” is just conditional on a world we take for granted.

We’re too obsessed with decomposing direct and indirect effects in mediation. "mediation should not be understood in terms of decomposition...Once the priority of research questions is established, the practical irrelevance of statistical effect decomposition directly follows" osf.io/preprints/ps...

Card & Krueger's (1994) minimum wage study may be such an extreme case of confounding: "State" (NJ vs. PA), a confounder, perfectly correlates with the causal variable "minimum wage." Their interest was in the effect of minimum wage on employment, not the effect of restaurants' state location.

Looking for a tool to more easily draw your DAGs and reason on them? Try PV-dagger (pvverse.github.io/pv_dagger/). Specifically designed by @fusarolimichele.bsky.social to deal with the complex DAGs involved in pharmacovigilance, helps positioning and color-coding confounds, measurement errors, etc

A key insight is the equivalence btw suppressors and instrumental variables. Yes, DAGs are useful for understanding why S is zero-related with Y, yet can increase the overall prediction.

This sounds like the same error I blogged about a few years ago, the common error of trying to control for population (or body size or many etc) by dividing the outcome variable by it. Props to the authors for seeking review and taking the issue seriously. Role models for us all.

Clear from the DAG, A² acts as an instrumental variable (conditional on A), enabling the identification of the M → Y effect even with U. This is what shorturl.at/1TgCm showed: mediation analysis can be valid (even with U) if the M model has a higher order of A than the Y model.

DAGs (causal graphs) can be used to understand the mechanics of linear interaction analysis. See more here: bpspsychub.onlinelibrary.wiley.com/doi/10.1111/...