2 citations · 2 across the 3 of their papers we have counts for
10 papers
As Good as it Gets: Bounds for Oracle Time-Varying Treatment Strategies
Zach Shahn
Much causal inference research is focused on methods for optimizing dynamic treatment regimes (Murphy, 2003; Robins, 2004; Schulte et al., 2015), which are rules for deciding which…
Structural Nested Mean Models Under Parallel Trends Assumptions
Zach Shahn, Oliver Dukes, Meghana Shamsunder +3
We link and extend two approaches to estimating time-varying treatment effects on repeated continuous outcomes--time-varying Difference in Differences (DiD; see Roth et al. (2023)…
Trust Me, I'm a Doctor?
Zach Shahn, Mats Stensrud
Clinical trials usually target average treatment effects, but treatment decisions are made for individuals. This tension motivates a common criticism of evidence-based medicine: a…
Structural Nested Mean Models Under Parallel Trends with Interference
Zach Shahn, Paul Zivich, Audrey Renson
Despite the common occurrence of interference in Difference-in-Differences (DiD) applications, standard DiD methods rely on an assumption that interference is absent, and comparati…
Identification and Estimation of Joint Potential Outcome Distributions from a Single Study
Zach Shahn, David Madigan
Most causal inference methods focus on estimating marginal average treatment effects, but many important causal estimands depend on the joint distribution of potential outcomes, in…
Structural Nested Mean Models for Modified Treatment Policies
Zach Shahn
There is a growing literature on estimating effects of treatment strategies based on the natural treatment that would have been received in the absence of intervention, often dubbe…