To me this is a depressing theme in modern academia.

There is so much work being produced, and so many competing demands on our time, that people rarely seem able to just closely read work and frankly say "yes, I believe this" or "no, I don't."

If we aren't doing this, what _are_ we doing?!

We show that the act of choosing an identification strategy implicitly expresses a belief about the degree of violations that must be present in alternative identification strategies. (4/4)

To help compare the sensitivities across the different identification strategies, we propose a set of sensitivity tools and an augmented bias contour plot visualizes the relationship between these strategies. (3/n)

We develop bias expressions for IV and proximal inference that show how violations of their respective assumptions are actually *amplified* by any unmeasured confounding in the outcome variable. (2/n)

Congratulations Anton!!!

Congratulations Guilherme!

Key features:
- Closed-form solution: The robust CATE is an interpretable weighted average of site-specific CATE models.
- Flexibility: Allows the use of off-the-shelf single-site CATE estimation methods.
- Privacy-preserving: Avoids sharing individual-level data across sites. (4/n)

We propose a minimax-regret framework for generalizing CATEs (conditional average treatment effects) across multisite data. We propose minimizing the worst-case regret over a class of target populations whose CATE can be represented as convex combinations of site-specific CATEs. (3/n)

Researchers often want to estimate HTEs that are consistent across different populations & contexts. When we have multiple source sites, we run into challenges:
(1) Site-specific models lack external validity.
(2) Pooled models risk bias if site heterogeneity is ignored. (2/n)