But I do agree that the addendum is progress from the previous state of things!

But more seriously, the addition would have been a sentence and a couple of references, not a complete framework, which already exists.

The better analogy is a bus that drops you off midway to your destination, not even in a bus stop, and steals your phone so you can’t get home 😉

I think the addendum fell short and should have described a need and approaches to mathematically define and identify estimands.

Thanks for sharing. Tangential to the thread but I have to say that I disagree with the statement of the abstract that causal inference estimands correspond to an effect in an ideal trial. Many valid scientific questions are expressed in terms of parameters that cannot be identified in trials!

What I do not follow about both TTE and addendum is why reinvent the wheel. I get it if it is about communicating to new audiences, but not if they are presented as new methods when they are so clearly inferior to what's already out there.
bsky.app/profile/idia...

TTE is certainly useful (I use it). But it is not a replacement for formal causal models + causal estimands + identification + optimal estimation etc. I recommend reading Maya Petersen and colleagues' papers on the "roadmap for causal inference."

Presenting TTEs as a method rather than as a communication tool had the unintended consequence of folks slapping the moniker in studies as a quality signifier without doing the actual leg work required to address the issues, discussing them, or even understanding them.

Right, IMO target trials are a great *communication tool* to talk with folks who do not have in-depth causal inference training (hence their success), but to really understand the issues one has to rely on standard theory (causal models, identification, estimands, optimal estimation, etc.)

Curious to hear if the gripes are substantive or if they are attribution-type (e.g., causal inference was using estimands long before the addendum).

It should be telling that it is the field of CI that has given folks the tools to understand the conditions under which observational studies deliver causal effects. You may argue whether those conditions are ever achievable, but criticizing CI for achieving its goal seems silly.

Clinician: How do I make sure Y(a) is independent of A conditional on covariates

Statistician: You measure all common causes of A and Y…

🤷🏻

I guess I would agree that Borges was pretentious:

www.youtube.com/watch?v=NJYo...

I think missing data is a CI problem (counterfactual is "would have observed the data") but not the opposite. E.g., recasting mediation analyses etc as a missing data problems seems contrived.

Cheap non alcoholic cava in a tetra pack container. How come the outcomes have to be “potential”?

At some point we’ll have to give these guys some agency 😉

Re: the OP, it also seems right to me that stats should focus on the properties of estimators, but the stubborn rejection of the language that links stats to science (CI) by some “trad” statisticians seems very odd to me

On this, I agree with Pearl and others who have emphasized that estimators aren’t causal. A causal estimand equals a statistical estimand (usually) under assumptions; estimators target statistical estimando which may or may not have a causal interpretation.