13 research outputs found
A proposed clinical and biological interpretation of mediated interaction
Understanding of causal pathways in epidemiology involves the concepts of direct and indirect effects. Recently, causal mediation analysis has been formalized to quantify these direct and indirect effects in the presence of exposure–mediator interaction and even allows for four-way decomposition of the total effect: controlled direct effect, reference interaction, mediated interaction, pure indirect effect. Whereas the other three effects can be intuitively conceptualized, mediated interaction is often considered a nuisance in statistical analysis. In this paper, we focus on mediated interaction and contrast it against pure mediation. We also propose a clinical and biological interpretation of mediated interaction using three hypothetical examples. With these examples we aim to make researchers aware that mediated interaction can actually provide important clinical and biological information
Semiparametric theory and empirical processes in causal inference
In this paper we review important aspects of semiparametric theory and
empirical processes that arise in causal inference problems. We begin with a
brief introduction to the general problem of causal inference, and go on to
discuss estimation and inference for causal effects under semiparametric
models, which allow parts of the data-generating process to be unrestricted if
they are not of particular interest (i.e., nuisance functions). These models
are very useful in causal problems because the outcome process is often complex
and difficult to model, and there may only be information available about the
treatment process (at best). Semiparametric theory gives a framework for
benchmarking efficiency and constructing estimators in such settings. In the
second part of the paper we discuss empirical process theory, which provides
powerful tools for understanding the asymptotic behavior of semiparametric
estimators that depend on flexible nonparametric estimators of nuisance
functions. These tools are crucial for incorporating machine learning and other
modern methods into causal inference analyses. We conclude by examining related
extensions and future directions for work in semiparametric causal inference
A review of spatial causal inference methods for environmental and epidemiological applications
The scientific rigor and computational methods of causal inference have had
great impacts on many disciplines, but have only recently begun to take hold in
spatial applications. Spatial casual inference poses analytic challenges due to
complex correlation structures and interference between the treatment at one
location and the outcomes at others. In this paper, we review the current
literature on spatial causal inference and identify areas of future work. We
first discuss methods that exploit spatial structure to account for unmeasured
confounding variables. We then discuss causal analysis in the presence of
spatial interference including several common assumptions used to reduce the
complexity of the interference patterns under consideration. These methods are
extended to the spatiotemporal case where we compare and contrast the potential
outcomes framework with Granger causality, and to geostatistical analyses
involving spatial random fields of treatments and responses. The methods are
introduced in the context of observational environmental and epidemiological
studies, and are compared using both a simulation study and analysis of the
effect of ambient air pollution on COVID-19 mortality rate. Code to implement
many of the methods using the popular Bayesian software OpenBUGS is provided
Partitioning the Heritability of Tourette Syndrome and Obsessive Compulsive Disorder Reveals Differences in Genetic Architecture
The direct estimation of heritability from genome-wide common variant data as implemented in the program Genome-wide Complex Trait Analysis (GCTA) has provided a means to quantify heritability attributable to all interrogated variants. We have quantified the variance in liability to disease explained
Variance component tests of multivariate mediation effects under composite null hypotheses
A Proof of Bell's Inequality in Quantum Mechanics Using Causal interactions
Analysis and Stochastic
A Mapping Between Interactions and Interference Implications for Vaccine Trials
Clinical epidemiolog
Large Sample Randomization Inference of Causal Effects in the Presence of Interference
Recently, increasing attention has focused on making causal inference when interference is possible. In the presence of interference, treatment may have several types of effects. In this paper, we consider inference about such effects when the population consists of groups of individuals where interference is possible within groups but not between groups. A two stage randomization design is assumed where in the first stage groups are randomized to different treatment allocation strategies and in the second stage individuals are randomized to treatment or control conditional on the strategy assigned to their group in the first stage. For this design, the asymptotic distributions of estimators of the causal effects are derived when either the number of individuals per group or the number of groups grows large. Under certain homogeneity assumptions, the asymptotic distributions provide justification for Wald-type confidence intervals (CIs) and tests. Empirical results demonstrate the Wald CIs have good coverage in finite samples and are narrower than CIs based on either the Chebyshev or Hoeffding inequalities provided the number of groups is not too small. The methods are illustrated by two examples which consider the effects of cholera vaccination and an intervention to encourage voting