Identification of causal effects on binary outcomes using structural mean models

Structural mean models (SMMs) were originally formulated to estimate causal effects among those selecting treatment in randomized controlled trials affected by nonignorable noncompliance. It has already been established that SMMs can identify these causal effects in randomized placebo-controlled trials under fairly weak assumptions. SMMs are now being used to analyze other types of study where identification depends on a no effect modification assumption. We highlight how this assumption depends crucially on the unknown causal model that generated the data, and so is difficult to justify. However, we also highlight that, if treatment selection is monotonic, additive and multiplicative SMMs do identify local (or complier) causal effects, but that the double-logistic SMM estimator does not without further assumptions. We clarify the proper interpretation of inferences from SMMs by means of an application and a simulation study. © 2010 The Author

Causal inference Methods for Addressing Censoring by Death and Unmeasured Confounding Using Instrumental Variables

This thesis considers three problems in causal inference. First, for the censoring by death problem, we propose a set of ranked average score assumptions making use of survival information both before and after the measurement of a non-mortality outcome to tighten the bounds on the survivor average causal effect (SACE) obtained in the previous literature that utilized survival information only before the measurement. We apply our method to a randomized trial study of the effect of mechanical ventilation with lower tidal volume vs. traditional tidal volume for acute lung injury patients. Our bounds on the SACE are much shorter than the bounds obtained using only the survival information before the measurement of the non-mortality outcome. Second, for the IV method with nonignorable missing covariates problem, we develop a method to estimate the causal effect of a treatment in observational studies using an IV when there are nonignorable missing covariates, i.e., missingness depending on the partially observed compliance class besides the fully observed outcome, covariates and IV. We apply our method to a motivating study in neonatal care to study the effectiveness of high level compared to low level NICUs. Third, besides the association with the treatment, there are two key assumptions for the IV to be valid: (i) the IV is essentially random conditioning on observed covariates, (ii) the IV affects outcomes only by altering the treatment, the so-called ``exclusion restriction . These two assumptions are often said to be untestable; however, that is untrue if testable means checking the compatibility of assumptions with other things we think we know. A test of this sort may result in an aporia. We discuss this subject in the context of our on-going study of the effects of delivery by cesarean section on the survival of extremely premature infants of 23-24 weeks gestational age

Survivor-complier effects in the presence of selection on treatment, with application to a study of prompt ICU admission

Pre-treatment selection or censoring (`selection on treatment') can occur when two treatment levels are compared ignoring the third option of neither treatment, in `censoring by death' settings where treatment is only defined for those who survive long enough to receive it, or in general in studies where the treatment is only defined for a subset of the population. Unfortunately, the standard instrumental variable (IV) estimand is not defined in the presence of such selection, so we consider estimating a new survivor-complier causal effect. Although this effect is generally not identified under standard IV assumptions, it is possible to construct sharp bounds. We derive these bounds and give a corresponding data-driven sensitivity analysis, along with nonparametric yet efficient estimation methods. Importantly, our approach allows for high-dimensional confounding adjustment, and valid inference even after employing machine learning. Incorporating covariates can tighten bounds dramatically, especially when they are strong predictors of the selection process. We apply the methods in a UK cohort study of critical care patients to examine the mortality effects of prompt admission to the intensive care unit, using ICU bed availability as an instrument

Treatment Evaluation With Multiple Outcome Periods Under Endogeneity and Attrition

This article develops a nonparametric methodology for treatment evaluation with multiple outcome periods under treatment endogeneity and missing outcomes. We use instrumental variables, pretreatment characteristics, and short-term (or intermediate) outcomes to identify the average treatment effect on the outcomes of compliers (the subpopulation whose treatment reacts on the instrument) in multiple periods based on inverse probability weighting. Treatment selection and attrition may depend on both observed characteristics and the unobservable compliancetype,whichispossiblyrelatedtounobservedfactors.Wealsoprovideasimulation studyandapplyourmethodstotheevaluation of a policy intervention targeting college achievement, where we find that controlling for attrition considerably affects the effect estimates. Supplementary materials for this article are available online