Combating unmeasured confounding in cross-sectional studies: Evaluating instrumental-variable and Heckman selection models.

Using an instrumental variable to test for unmeasured confounding.

An important concern in an observational study is whether or not there is unmeasured confounding, that is, unmeasured ways in which the treatment and control groups differ before treatment, which affect the outcome. We develop a test of whether there is unmeasured confounding when an instrumental variable (IV) is available. An IV is a variable that is independent of the unmeasured confounding a...

Department of Economics Cross - sectional Averaging and Instrumental Variable Estimation

Propensity Score Adjustment for Unmeasured Confounding in Observational Studies

Adjusting for several unmeasured confounders is a challenging problem in the analysis of observational data. Information about unmeasured confounders is sometimes available from external validation data, such as surveys or secondary samples drawn from the same source population. In principal, the validation permits us to recover information about the missing data, but the difficulty is in elici...

Bayesian sensitivity analysis for unmeasured confounding in observational studies.

We consider Bayesian sensitivity analysis for unmeasured confounding in observational studies where the association between a binary exposure, binary response, measured confounders and a single binary unmeasured confounder can be formulated using logistic regression models. A model for unmeasured confounding is presented along with a family of prior distributions that model beliefs about a poss...

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 ...