Principal stratification analysis using principal scores
release_p5ybcsulgnfd7cdocp2qvcjsqi
by
Peng Ding, Jiannan Lu
2016
Abstract
Practitioners are interested in not only the average causal effect of the
treatment on the outcome but also the underlying causal mechanism in the
presence of an intermediate variable between the treatment and outcome.
However, in many cases we cannot randomize the intermediate variable, resulting
in sample selection problems even in randomized experiments. Therefore, we view
randomized experiments with intermediate variables as semi-observational
studies. In parallel with the analysis of observational studies, we provide a
theoretical foundation for conducting objective causal inference with an
intermediate variable under the principal stratification framework, with
principal strata defined as the joint potential values of the intermediate
variable. Our strategy constructs weighted samples based on principal scores,
defined as the conditional probabilities of the latent principal strata given
covariates, without access to any outcome data. This principal stratification
analysis yields robust causal inference without relying on any model
assumptions on the outcome distributions. We also propose approaches to
conducting sensitivity analysis for violations of the ignorability and
monotonicity assumptions, the very crucial but untestable identification
assumptions in our theory. When the assumptions required by the classical
instrumental variable analysis cannot be justified by background knowledge or
cannot be made because of scientific questions of interest, our strategy serves
as a useful alternative tool to deal with intermediate variables. We illustrate
our methodologies by using two real data examples, and find scientifically
meaningful conclusions.
In text/plain
format
Archived Files and Locations
application/pdf 556.0 kB
file_ufqarg77vjgttnjafh6nfjdx44
|
arxiv.org (repository) web.archive.org (webarchive) |
1602.01196v1
access all versions, variants, and formats of this works (eg, pre-prints)