Instrumental variable methods for causal inference

Regression and Weighting Methods for Causal Inference Using Instrumental Variables

Recent researches in econometrics and statistics have gained considerable insights into the use of instrumental variables (IVs) for causal inference. A basic idea is that IVs serve as an experimental handle, the turning of which may change each individual’s treatment status and, through and only through this effect, also change observed outcome. The average difference in observed outcome relati...

Mendelian randomization as an instrumental variable approach to causal inference.

In epidemiological research, the causal effect of a modifiable phenotype or exposure on a disease is often of public health interest. Randomized controlled trials to investigate this effect are not always possible and inferences based on observational data can be confounded. However, if we know of a gene closely linked to the phenotype without direct effect on the disease, it can often be reaso...

Matching Methods for Causal Inference

Shrinkage methods for instrumental variable estimation∗

This paper proposes shrinkage methods for instrumental variable estimation to solve the “many instruments” problem. Even though using a large number of instruments reduces the asymptotic variances of the estimators, it has been observed both in theoretical works and in practice that in finite samples the estimators may behave very poorly if the number of instruments is large. This problem can b...

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 ...