179 research outputs found
Fair Inference On Outcomes
In this paper, we consider the problem of fair statistical inference
involving outcome variables. Examples include classification and regression
problems, and estimating treatment effects in randomized trials or
observational data. The issue of fairness arises in such problems where some
covariates or treatments are "sensitive," in the sense of having potential of
creating discrimination. In this paper, we argue that the presence of
discrimination can be formalized in a sensible way as the presence of an effect
of a sensitive covariate on the outcome along certain causal pathways, a view
which generalizes (Pearl, 2009). A fair outcome model can then be learned by
solving a constrained optimization problem. We discuss a number of
complications that arise in classical statistical inference due to this view
and provide workarounds based on recent work in causal and semi-parametric
inference
Semiparametric theory for causal mediation analysis: Efficiency bounds, multiple robustness and sensitivity analysis
While estimation of the marginal (total) causal effect of a point exposure on
an outcome is arguably the most common objective of experimental and
observational studies in the health and social sciences, in recent years,
investigators have also become increasingly interested in mediation analysis.
Specifically, upon evaluating the total effect of the exposure, investigators
routinely wish to make inferences about the direct or indirect pathways of the
effect of the exposure, through a mediator variable or not, that occurs
subsequently to the exposure and prior to the outcome. Although powerful
semiparametric methodologies have been developed to analyze observational
studies that produce double robust and highly efficient estimates of the
marginal total causal effect, similar methods for mediation analysis are
currently lacking. Thus, this paper develops a general semiparametric framework
for obtaining inferences about so-called marginal natural direct and indirect
causal effects, while appropriately accounting for a large number of
pre-exposure confounding factors for the exposure and the mediator variables.
Our analytic framework is particularly appealing, because it gives new insights
on issues of efficiency and robustness in the context of mediation analysis. In
particular, we propose new multiply robust locally efficient estimators of the
marginal natural indirect and direct causal effects, and develop a novel double
robust sensitivity analysis framework for the assumption of ignorability of the
mediator variable.Comment: Published in at http://dx.doi.org/10.1214/12-AOS990 the Annals of
Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical
Statistics (http://www.imstat.org
Semiparametric Causal Sufficient Dimension Reduction Of High Dimensional Treatments
Cause-effect relationships are typically evaluated by comparing the outcome
responses to binary treatment values, representing two arms of a hypothetical
randomized controlled trial. However, in certain applications, treatments of
interest are continuous and high dimensional. For example, understanding the
causal relationship between severity of radiation therapy, represented by a
high dimensional vector of radiation exposure values and post-treatment side
effects is a problem of clinical interest in radiation oncology. An appropriate
strategy for making interpretable causal conclusions is to reduce the dimension
of treatment. If individual elements of a high dimensional treatment vector
weakly affect the outcome, but the overall relationship between the treatment
variable and the outcome is strong, careless approaches to dimension reduction
may not preserve this relationship. Moreover, methods developed for regression
problems do not transfer in a straightforward way to causal inference due to
confounding complications between the treatment and outcome. In this paper, we
use semiparametric inference theory for structural models to give a general
approach to causal sufficient dimension reduction of a high dimensional
treatment such that the cause-effect relationship between the treatment and
outcome is preserved. We illustrate the utility of our proposal through
simulations and a real data application in radiation oncology
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