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Stronger instruments and refined covariate balance in an observational study of the effectiveness of prompt admission to intensive care units
Instrumental variable methods, subject to appropriate identification assumptions, enable consistent estimation of causal effects in the presence of unobserved confounding. Near–far matching has been proposed as one analytic method to improve inference by strengthening the effect of the instrument on the exposure and balancing observable characteristics between groups of subjects with low and high values of the instrument. However, in settings with hierarchical data (e.g. patients nested within hospitals), or where several covariate interactions must be balanced, conventional near–far matching algorithms may fail to achieve the requisite covariate balance. We develop a new matching algorithm, that combines near–far matching with refined covariate balance, to balance large numbers of nominal covariates while also strengthening the instrumental variable. This extension of near–far matching is motivated by a case-study that aims to identify the causal effect of prompt admission to an intensive care unit on 7-day and 28-day mortality
Developing a distributed electronic health-record store for India
The DIGHT project is addressing the problem of building a scalable and highly available information store for the Electronic Health Records (EHRs) of the over one billion citizens of India
Methodological concepts for integrated assessment of agricultural and environmental policies in SEAMLESS-IF
Agricultural and Food Policy, Environmental Economics and Policy, Farm Management, Land Economics/Use,
Self-tuned Visual Subclass Learning with Shared Samples An Incremental Approach
Computer vision tasks are traditionally defined and evaluated using semantic
categories. However, it is known to the field that semantic classes do not
necessarily correspond to a unique visual class (e.g. inside and outside of a
car). Furthermore, many of the feasible learning techniques at hand cannot
model a visual class which appears consistent to the human eye. These problems
have motivated the use of 1) Unsupervised or supervised clustering as a
preprocessing step to identify the visual subclasses to be used in a
mixture-of-experts learning regime. 2) Felzenszwalb et al. part model and other
works model mixture assignment with latent variables which is optimized during
learning 3) Highly non-linear classifiers which are inherently capable of
modelling multi-modal input space but are inefficient at the test time. In this
work, we promote an incremental view over the recognition of semantic classes
with varied appearances. We propose an optimization technique which
incrementally finds maximal visual subclasses in a regularized risk
minimization framework. Our proposed approach unifies the clustering and
classification steps in a single algorithm. The importance of this approach is
its compliance with the classification via the fact that it does not need to
know about the number of clusters, the representation and similarity measures
used in pre-processing clustering methods a priori. Following this approach we
show both qualitatively and quantitatively significant results. We show that
the visual subclasses demonstrate a long tail distribution. Finally, we show
that state of the art object detection methods (e.g. DPM) are unable to use the
tails of this distribution comprising 50\% of the training samples. In fact we
show that DPM performance slightly increases on average by the removal of this
half of the data.Comment: Updated ICCV 2013 submissio
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