48,464 research outputs found
Estimating Causal Direct and Indirect Effects in the Presence of Post-Treatment Confounders: A Simulation Study
abstract: In investigating mediating processes, researchers usually use randomized experiments and linear regression or structural equation modeling to determine if the treatment affects the hypothesized mediator and if the mediator affects the targeted outcome. However, randomizing the treatment will not yield accurate causal path estimates unless certain assumptions are satisfied. Since randomization of the mediator may not be plausible for most studies (i.e., the mediator status is not randomly assigned, but self-selected by participants), both the direct and indirect effects may be biased by confounding variables. The purpose of this dissertation is (1) to investigate the extent to which traditional mediation methods are affected by confounding variables and (2) to assess the statistical performance of several modern methods to address confounding variable effects in mediation analysis. This dissertation first reviewed the theoretical foundations of causal inference in statistical mediation analysis, modern statistical analysis for causal inference, and then described different methods to estimate causal direct and indirect effects in the presence of two post-treatment confounders. A large simulation study was designed to evaluate the extent to which ordinary regression and modern causal inference methods are able to obtain correct estimates of the direct and indirect effects when confounding variables that are present in the population are not included in the analysis. Five methods were compared in terms of bias, relative bias, mean square error, statistical power, Type I error rates, and confidence interval coverage to test how robust the methods are to the violation of the no unmeasured confounders assumption and confounder effect sizes. The methods explored were linear regression with adjustment, inverse propensity weighting, inverse propensity weighting with truncated weights, sequential g-estimation, and a doubly robust sequential g-estimation. Results showed that in estimating the direct and indirect effects, in general, sequential g-estimation performed the best in terms of bias, Type I error rates, power, and coverage across different confounder effect, direct effect, and sample sizes when all confounders were included in the estimation. When one of the two confounders were omitted from the estimation process, in general, none of the methods had acceptable relative bias in the simulation study. Omitting one of the confounders from estimation corresponded to the common case in mediation studies where no measure of a confounder is available but a confounder may affect the analysis. Failing to measure potential post-treatment confounder variables in a mediation model leads to biased estimates regardless of the analysis method used and emphasizes the importance of sensitivity analysis for causal mediation analysis.Dissertation/ThesisPh.D. Psychology 201
Causal graphical models in systems genetics: A unified framework for joint inference of causal network and genetic architecture for correlated phenotypes
Causal inference approaches in systems genetics exploit quantitative trait
loci (QTL) genotypes to infer causal relationships among phenotypes. The
genetic architecture of each phenotype may be complex, and poorly estimated
genetic architectures may compromise the inference of causal relationships
among phenotypes. Existing methods assume QTLs are known or inferred without
regard to the phenotype network structure. In this paper we develop a
QTL-driven phenotype network method (QTLnet) to jointly infer a causal
phenotype network and associated genetic architecture for sets of correlated
phenotypes. Randomization of alleles during meiosis and the unidirectional
influence of genotype on phenotype allow the inference of QTLs causal to
phenotypes. Causal relationships among phenotypes can be inferred using these
QTL nodes, enabling us to distinguish among phenotype networks that would
otherwise be distribution equivalent. We jointly model phenotypes and QTLs
using homogeneous conditional Gaussian regression models, and we derive a
graphical criterion for distribution equivalence. We validate the QTLnet
approach in a simulation study. Finally, we illustrate with simulated data and
a real example how QTLnet can be used to infer both direct and indirect effects
of QTLs and phenotypes that co-map to a genomic region.Comment: Published in at http://dx.doi.org/10.1214/09-AOAS288 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
On Discrimination Discovery and Removal in Ranked Data using Causal Graph
Predictive models learned from historical data are widely used to help
companies and organizations make decisions. However, they may digitally
unfairly treat unwanted groups, raising concerns about fairness and
discrimination. In this paper, we study the fairness-aware ranking problem
which aims to discover discrimination in ranked datasets and reconstruct the
fair ranking. Existing methods in fairness-aware ranking are mainly based on
statistical parity that cannot measure the true discriminatory effect since
discrimination is causal. On the other hand, existing methods in causal-based
anti-discrimination learning focus on classification problems and cannot be
directly applied to handle the ranked data. To address these limitations, we
propose to map the rank position to a continuous score variable that represents
the qualification of the candidates. Then, we build a causal graph that
consists of both the discrete profile attributes and the continuous score. The
path-specific effect technique is extended to the mixed-variable causal graph
to identify both direct and indirect discrimination. The relationship between
the path-specific effects for the ranked data and those for the binary decision
is theoretically analyzed. Finally, algorithms for discovering and removing
discrimination from a ranked dataset are developed. Experiments using the real
dataset show the effectiveness of our approaches.Comment: 9 page
Identification, Inference and Sensitivity Analysis for Causal Mediation Effects
Causal mediation analysis is routinely conducted by applied researchers in a
variety of disciplines. The goal of such an analysis is to investigate
alternative causal mechanisms by examining the roles of intermediate variables
that lie in the causal paths between the treatment and outcome variables. In
this paper we first prove that under a particular version of sequential
ignorability assumption, the average causal mediation effect (ACME) is
nonparametrically identified. We compare our identification assumption with
those proposed in the literature. Some practical implications of our
identification result are also discussed. In particular, the popular estimator
based on the linear structural equation model (LSEM) can be interpreted as an
ACME estimator once additional parametric assumptions are made. We show that
these assumptions can easily be relaxed within and outside of the LSEM
framework and propose simple nonparametric estimation strategies. Second, and
perhaps most importantly, we propose a new sensitivity analysis that can be
easily implemented by applied researchers within the LSEM framework. Like the
existing identifying assumptions, the proposed sequential ignorability
assumption may be too strong in many applied settings. Thus, sensitivity
analysis is essential in order to examine the robustness of empirical findings
to the possible existence of an unmeasured confounder. Finally, we apply the
proposed methods to a randomized experiment from political psychology. We also
make easy-to-use software available to implement the proposed methods.Comment: Published in at http://dx.doi.org/10.1214/10-STS321 the Statistical
Science (http://www.imstat.org/sts/) by the Institute of Mathematical
Statistics (http://www.imstat.org
Comment: Complex Causal Questions Require Careful Model Formulation: Discussion of Rubin on Experiments with "Censoring" Due to Death
Comment on Complex Causal Questions Require Careful Model Formulation:
Discussion of Rubin on Experiments with ``Censoring'' Due to Death
[math.ST/0612783]Comment: Published at http://dx.doi.org/10.1214/088342306000000295 in the
Statistical Science (http://www.imstat.org/sts/) by the Institute of
Mathematical Statistics (http://www.imstat.org
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