I mean, without major assumptions.

Interesting that out of the MHE's bag of tricks approach, the only design that definitely identifies a basic interventional estimand such as the ATE is SOB. Imbens & Angrist's IV identifies the CACE, RDDs identify cutoff estimands, and DID identifies ATT, but none of them identifies the ATE.

If you have a massive disagreement on which DAGs were valid, but you don't use or get DAGs, then the work to solve those disagreement is just herculean

Lisp is so great!

Obrigado mesmo, Eduardo. Aprendi muito com os seus papers.

Thank you Jacob for your words

Thank you so much Lorena!

Obrigado mesmo Jamil. Fiquei ausente por um tempo por causa do market. Mas logo logo vamos tomar um café

Thank you so much Melody. And thanks a lot for your help during this process

Thank you so much Mike. It was really great to meet you at Polmeth

Obrigado mesmo Guilherme!!!

Congrats Anton! This is excellent for you and for Madison!

Just did, Claudia. Good to see you here.

A clear example is the Russian Roulette case proposed by Anders Huitfeldt, and then studied by Pearl and Cinelli (2021). Unfortunately, Anders, Carlos Cinelli, and Pearl don't seem to be here on bsky

In fact, no regression method can ensure external validity by itself. You need structural and sometimes functional assumptions. I feel like people in causal inference usually use the CATE invariance assumption. But in many cases this is not 100% guaranteed.