32 research outputs found
Graphical methods for inequality constraints in marginalized DAGs
We present a graphical approach to deriving inequality constraints for
directed acyclic graph (DAG) models, where some variables are unobserved. In
particular we show that the observed distribution of a discrete model is always
restricted if any two observed variables are neither adjacent in the graph, nor
share a latent parent; this generalizes the well known instrumental inequality.
The method also provides inequalities on interventional distributions, which
can be used to bound causal effects. All these constraints are characterized in
terms of a new graphical separation criterion, providing an easy and intuitive
method for their derivation.Comment: A final version will appear in the proceedings of the 22nd Workshop
on Machine Learning and Signal Processing, 201
Quantum violations in the Instrumental scenario and their relations to the Bell scenario
The causal structure of any experiment implies restrictions on the observable
correlations between measurement outcomes, which are different for experiments
exploiting classical, quantum, or post-quantum resources. In the study of Bell
nonlocality, these differences have been explored in great detail for more and
more involved causal structures. Here, we go in the opposite direction and
identify the simplest causal structure which exhibits a separation between
classical, quantum, and post-quantum correlations. It arises in the so-called
Instrumental scenario, known from classical causal models. We derive
inequalities for this scenario and show that they are closely related to
well-known Bell inequalities, such as the Clauser-Horne-Shimony-Holt
inequality, which enables us to easily identify their classical, quantum, and
post-quantum bounds as well as strategies violating the first two. The
relations that we uncover imply that the quantum or post-quantum advantages
witnessed by the violation of our Instrumental inequalities are not
fundamentally different from those witnessed by the violations of standard
inequalities in the usual Bell scenario. However, non-classical tests in the
Instrumental scenario require fewer input choices than their Bell scenario
counterpart, which may have potential implications for device-independent
protocols.Comment: 12 pages, 3 figures. Comments welcome! v4: published version in
Quantum journa
Experimental device-independent certified randomness generation with an instrumental causal structure
The intrinsic random nature of quantum physics offers novel tools for the
generation of random numbers, a central challenge for a plethora of fields.
Bell non-local correlations obtained by measurements on entangled states allow
for the generation of bit strings whose randomness is guaranteed in a
device-independent manner, i.e. without assumptions on the measurement and
state-generation devices. Here, we generate this strong form of certified
randomness on a new platform: the so-called instrumental scenario, which is
central to the field of causal inference. First, we theoretically show that
certified random bits, private against general quantum adversaries, can be
extracted exploiting device-independent quantum instrumental-inequality
violations. To that end, we adapt techniques previously developed for the Bell
scenario. Then, we experimentally implement the corresponding
randomness-generation protocol using entangled photons and active feed-forward
of information. Moreover, we show that, for low levels of noise, our protocol
offers an advantage over the simplest Bell-nonlocality protocol based on the
Clauser-Horn-Shimony-Holt inequality.Comment: Modified Supplementary Information: removed description of extractor
algorithm introduced by arXiv:1212.0520. Implemented security of the protocol
against general adversarial attack
Graphs for margins of Bayesian networks
Directed acyclic graph (DAG) models, also called Bayesian networks, impose
conditional independence constraints on a multivariate probability
distribution, and are widely used in probabilistic reasoning, machine learning
and causal inference. If latent variables are included in such a model, then
the set of possible marginal distributions over the remaining (observed)
variables is generally complex, and not represented by any DAG. Larger classes
of mixed graphical models, which use multiple edge types, have been introduced
to overcome this; however, these classes do not represent all the models which
can arise as margins of DAGs. In this paper we show that this is because
ordinary mixed graphs are fundamentally insufficiently rich to capture the
variety of marginal models.
We introduce a new class of hyper-graphs, called mDAGs, and a latent
projection operation to obtain an mDAG from the margin of a DAG. We show that
each distinct marginal of a DAG model is represented by at least one mDAG, and
provide graphical results towards characterizing when two such marginal models
are the same. Finally we show that mDAGs correctly capture the marginal
structure of causally-interpreted DAGs under interventions on the observed
variables
Assumptions of IV Methods for Observational Epidemiology
Instrumental variable (IV) methods are becoming increasingly popular as they
seem to offer the only viable way to overcome the problem of unobserved
confounding in observational studies. However, some attention has to be paid to
the details, as not all such methods target the same causal parameters and some
rely on more restrictive parametric assumptions than others. We therefore
discuss and contrast the most common IV approaches with relevance to typical
applications in observational epidemiology. Further, we illustrate and compare
the asymptotic bias of these IV estimators when underlying assumptions are
violated in a numerical study. One of our conclusions is that all IV methods
encounter problems in the presence of effect modification by unobserved
confounders. Since this can never be ruled out for sure, we recommend that
practical applications of IV estimators be accompanied routinely by a
sensitivity analysis.Comment: Published in at http://dx.doi.org/10.1214/09-STS316 the Statistical
Science (http://www.imstat.org/sts/) by the Institute of Mathematical
Statistics (http://www.imstat.org