1,961 research outputs found
Targeted Maximum Likelihood Estimation using Exponential Families
Targeted maximum likelihood estimation (TMLE) is a general method for
estimating parameters in semiparametric and nonparametric models. Each
iteration of TMLE involves fitting a parametric submodel that targets the
parameter of interest. We investigate the use of exponential families to define
the parametric submodel. This implementation of TMLE gives a general approach
for estimating any smooth parameter in the nonparametric model. A computational
advantage of this approach is that each iteration of TMLE involves estimation
of a parameter in an exponential family, which is a convex optimization problem
for which software implementing reliable and computationally efficient methods
exists. We illustrate the method in three estimation problems, involving the
mean of an outcome missing at random, the parameter of a median regression
model, and the causal effect of a continuous exposure, respectively. We conduct
a simulation study comparing different choices for the parametric submodel,
focusing on the first of these problems. To the best of our knowledge, this is
the first study investigating robustness of TMLE to different specifications of
the parametric submodel. We find that the choice of submodel can have an
important impact on the behavior of the estimator in finite samples
Non-agency interventions for causal mediation in the presence of intermediate confounding
Recent approaches to causal inference have focused on causal effects defined
as contrasts between the distribution of counterfactual outcomes under
hypothetical interventions on the nodes of a graphical model. In this article
we develop theory for causal effects defined with respect to a different type
of intervention, one which alters the information propagated through the edges
of the graph. These information transfer interventions may be more useful than
node interventions in settings in which causes are non-manipulable, for example
when considering race or genetics as a causal agent. Furthermore, information
transfer interventions allow us to define path-specific decompositions which
are identified in the presence of treatment-induced mediator-outcome
confounding, a practical problem whose general solution remains elusive. We
prove that the proposed effects provide valid statistical tests of mechanisms,
unlike popular methods based on randomized interventions on the mediator. We
propose efficient non-parametric estimators for a covariance version of the
proposed effects, using data-adaptive regression coupled with semi-parametric
efficiency theory to address model misspecification bias while retaining
-consistency and asymptotic normality. We illustrate the use of our
methods in two examples using publicly available data
DermaKNet: Incorporating the Knowledge of Dermatologists to Convolutional Neural Networks for Skin Lesion Diagnosis
Traditional approaches to automatic diagnosis of skin lesions consisted of classifiers working on sets of hand-crafted features, some of which modeled lesion aspects of special importance for dermatologists. Recently, the broad adoption of convolutional neural networks (CNNs) in most computer vision tasks has brought about a great leap forward in terms of performance. Nevertheless, with this performance leap, the CNN-based computer-aided diagnosis (CAD) systems have also brought a notable reduction of the useful insights provided by hand-crafted features. This paper presents DermaKNet, a CAD system based on CNNs that incorporates specific subsystems modeling properties of skin lesions that are of special interest to dermatologists aiming to improve the interpretability of its diagnosis. Our results prove that the incorporation of these subsystems not only improves the performance, but also enhances the diagnosis by providing more interpretable outputs.This work was
supported in part by the National Grant TEC2014-53390-P and National
Grant TEC2014-61729-EXP of the Spanish Ministry of Economy and
Competitiveness, and in part by NVIDIA Corporation with the donation
of the TITAN X GPUPublicad
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