92,749 research outputs found
Learning Counterfactual Representations for Estimating Individual Dose-Response Curves
Estimating what would be an individual's potential response to varying levels
of exposure to a treatment is of high practical relevance for several important
fields, such as healthcare, economics and public policy. However, existing
methods for learning to estimate counterfactual outcomes from observational
data are either focused on estimating average dose-response curves, or limited
to settings with only two treatments that do not have an associated dosage
parameter. Here, we present a novel machine-learning approach towards learning
counterfactual representations for estimating individual dose-response curves
for any number of treatments with continuous dosage parameters with neural
networks. Building on the established potential outcomes framework, we
introduce performance metrics, model selection criteria, model architectures,
and open benchmarks for estimating individual dose-response curves. Our
experiments show that the methods developed in this work set a new
state-of-the-art in estimating individual dose-response
Learning loopy graphical models with latent variables: Efficient methods and guarantees
The problem of structure estimation in graphical models with latent variables
is considered. We characterize conditions for tractable graph estimation and
develop efficient methods with provable guarantees. We consider models where
the underlying Markov graph is locally tree-like, and the model is in the
regime of correlation decay. For the special case of the Ising model, the
number of samples required for structural consistency of our method scales
as , where p is the
number of variables, is the minimum edge potential, is
the depth (i.e., distance from a hidden node to the nearest observed nodes),
and is a parameter which depends on the bounds on node and edge
potentials in the Ising model. Necessary conditions for structural consistency
under any algorithm are derived and our method nearly matches the lower bound
on sample requirements. Further, the proposed method is practical to implement
and provides flexibility to control the number of latent variables and the
cycle lengths in the output graph.Comment: Published in at http://dx.doi.org/10.1214/12-AOS1070 the Annals of
Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical
Statistics (http://www.imstat.org
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