Identification of Causal Effects Within Principal Strata Using Auxiliary Variables

Designs combining instrumental variables with case-control: estimating principal strata causal effects.

The instrumental variables framework is commonly used for the estimation of causal effects from cohort samples. However, the combination of instrumental variables with more efficient designs such as case-control sampling requires new methodological consideration. For example, as the use of Mendelian randomization studies is increasing and the cost of genotyping and gene expression data can be h...

Randomization-Based Inference within Principal Strata.

In randomized studies, treatment comparisons conditional on intermediate post-randomization outcomes using standard analytic methods do not have a causal interpretation. An alternate approach entails treatment comparisons within principal strata defined by the potential outcomes for the intermediate outcome that would be observed under each treatment assignment. In this paper, we develop method...

Causal Inference with Intermediates: Simple Methods for Principal Strata Effects and Natural Direct Effects

Exact Sampling Using Auxiliary Variables

Using Descendants as Instrumental Variables for the Identification of Direct Causal Effects in Linear SEMs

In this paper, we present an extended set of graphical criteria for the identification of direct causal effects in linear Structural Equation Models (SEMs). Previous methods of graphical identification of direct causal effects in linear SEMs include methods such as the single-door criterion, the instrumental variable and the IV-pair, and the accessory set. However, there remain graphical models...