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)