Year: 2020
Type: preprint
Abstract: Nonignorable missingness and noncompliance can occur even in well-designed randomized experiments making the intervention effect that the experiment was designed to estimate nonidentifiable. Nonparametric causal bounds provide a way to narrow the range of possible values for a nonidentifiable causal effect with minimal assumptions. We derive novel bounds for the causal risk difference for a binary outcome and intervention in randomized experiments with nonignorable missingness caused by a variety of mechanisms and with or without noncompliance. We illustrate the use of the proposed bounds in our motivating data example of peanut consumption on the development of peanut allergies in infants.
Source: arXiv (Cornell University)
Institution: Karolinska Institutet
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
Funder: Vetenskapsrådet
Awards: Development and application of statistical methods for register-based research, Risk factors for neurodevelopmental problems: development of novel statistical methods to estimate and compare the public health impact., Biostatistical methods for surrogate endpoints: advancing medical research and improving public health
OpenAlex ID: W4287643247