3 citations · 8 across the 17 of their papers we have counts for
15 papers · 1 filter
Difference-in-Differences for Heterogeneous Effects of General Treatment Strategies Accounting for Time-Varying Covariates
Oliver Dukes, Zach Shahn, James Robins +1
We introduce a class of marginal Structural Nested Mean Models (mSNMMs) that enable modeling time-varying effect modification by a subset of covariates required as adjustment varia…
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…
A Note on The Rationale Behind Using Parental Longevity as a Proxy in Mendelian Randomization Studies
Zach Shahn, Rehana Rasul, C. Mary Schooling
In many cohorts (such as the UK Biobank) on which Mendelian Randomization studies are routinely performed, data on participants' longevity is inadequate as the majority of particip…
Using causal diagrams to assess parallel trends in difference-in-differences studies
Audrey Renson, Oliver Dukes, Zach Shahn
Difference-in-differences (DID) is popular because it can allow for unmeasured confounding when the key assumption of parallel trends holds. However, there exists little guidance o…
Generalizing Difference-in-Differences to Non-Canonical Settings: Identifying an Array of Estimands
Zach Shahn, Laura Hatfield
Consider a general setting in which data on an outcome is collected in two `groups' at two time periods, with certain group-periods deemed `treated' and others `untreated'. A speci…
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…
Estimating Heterogeneous Treatment Effects on Survival Outcomes Using Counterfactual Censoring Unbiased Transformations
Shenbo Xu, Raluca Cobzaru, Stan N. Finkelstein +3
Methods for estimating heterogeneous treatment effects (HTE) from observational data have largely focused on continuous or binary outcomes, with less attention paid to survival out…
Subgroup Difference in Differences to Identify Effect Modification Without a Control Group
Zach Shahn
Suppose it is of interest to characterize effect heterogeneity of an intervention across levels of a baseline covariate using only pre- and post- intervention outcome measurements…
Efficient estimation of weighted cumulative treatment effects by double/debiased machine learning
Shenbo Xu, Bang Zheng, Bowen Su +5
In empirical studies with time-to-event outcomes, investigators often leverage observational data to conduct causal inference on the effect of exposure when randomized controlled t…
When Do Outcome Driven Treatments Break Parallel Trends?
Zach Shahn
Under what circumstances is it a threat to the parallel trends assumption required for Difference in Differences (DiD) studies if treatment decisions are based on past values of th…
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)…
Using negative controls to identify causal effects with invalid instrumental variables
Oliver Dukes, David B. Richardson, Zachary Shahn +2
Many proposals for the identification of causal effects require an instrumental variable that satisfies strong, untestable unconfoundedness and exclusion restriction assumptions. I…
A Formal Causal Interpretation of the Case-Crossover Design
Zach Shahn, Miguel A. Hernan, James M. Robins
The case-crossover design (Maclure, 1991) is widely used in epidemiology and other fields to study causal effects of transient treatments on acute outcomes. However, its validity a…
Efficient estimation of optimal regimes under a no direct effect assumption
Lin Liu, Zach Shahn, James M. Robins +1
We derive new estimators of an optimal joint testing and treatment regime under the no direct effect (NDE) assumption that a given laboratory, diagnostic, or screening test has no…