Handling Missing Data in 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...

Missing data as a causal inference problem

We address the problem of deciding whether there exists an unbiased estimator of a given relation Q, when data are missing not at random. We employ a formal representation called ‘Missingness Graphs’ to explicitly portray the causal mechanisms responsible for missingness and to encode dependencies between these mechanisms and the variables being measured. Using this representation, we define th...

A Review of Missing Data Handling Methods

Most of the real world datasets suffer from the problem of missing data. It may lead data mining analysts to end with wrong inferences about data under study. Many researchers are working on this problem to introduce more sophisticated methods. Eventhough many methods are available, analysts are facing difficulty in searching a suitable method due to lack of knowledge about the methods and thei...

Handling Missing and Corrupted Data in High-Dimensional Inference