70,646 research outputs found
Nested Markov Properties for Acyclic Directed Mixed Graphs
Directed acyclic graph (DAG) models may be characterized in at least four
different ways: via a factorization, the d-separation criterion, the
moralization criterion, and the local Markov property. As pointed out by Robins
(1986, 1999), Verma and Pearl (1990), and Tian and Pearl (2002b), marginals of
DAG models also imply equality constraints that are not conditional
independences. The well-known `Verma constraint' is an example. Constraints of
this type were used for testing edges (Shpitser et al., 2009), and an efficient
marginalization scheme via variable elimination (Shpitser et al., 2011).
We show that equality constraints like the `Verma constraint' can be viewed
as conditional independences in kernel objects obtained from joint
distributions via a fixing operation that generalizes conditioning and
marginalization. We use these constraints to define, via Markov properties and
a factorization, a graphical model associated with acyclic directed mixed
graphs (ADMGs). We show that marginal distributions of DAG models lie in this
model, prove that a characterization of these constraints given in (Tian and
Pearl, 2002b) gives an alternative definition of the model, and finally show
that the fixing operation we used to define the model can be used to give a
particularly simple characterization of identifiable causal effects in hidden
variable graphical causal models.Comment: 67 pages (not including appendix and references), 8 figure
Time as a guide to cause
How do people learn causal structure? In two studies we investigated
the interplay between temporal order, intervention and covariational cues. In
Study 1 temporal order overrode covariation information, leading to spurious
causal inferences when the temporal cues were misleading. In Study 2 both
temporal order and intervention contributed to accurate causal inference, well
beyond that achievable through covariational data alone. Together the studies
show that people use both temporal order and interventional cues to infer
causal structure, and that these cues dominate the available statistical
information. We endorse a hypothesis-driven account of learning, whereby
people use cues such as temporal order to generate initial models, and then
test these models against the incoming covariational data
Smooth, identifiable supermodels of discrete DAG models with latent variables
We provide a parameterization of the discrete nested Markov model, which is a
supermodel that approximates DAG models (Bayesian network models) with latent
variables. Such models are widely used in causal inference and machine
learning. We explicitly evaluate their dimension, show that they are curved
exponential families of distributions, and fit them to data. The
parameterization avoids the irregularities and unidentifiability of latent
variable models. The parameters used are all fully identifiable and
causally-interpretable quantities.Comment: 30 page
Temporal and causal reasoning in deaf and hearing novice readers
Temporal and causal information in text are crucial in helping the reader form a coherent representation of a narrative. Deaf novice readers are generally poor at processing linguistic markers of causal/temporal information (i.e., connectives), but what is unclear is whether this is indicative of a more general deficit in reasoning about temporal/causal information. In Study 1, 10 deaf and 63 hearing children, matched for comprehension ability and age, were compared on a range of tasks tapping temporal/causal reasoning skills. In Study 2, 20 deaf and 32 hearing children, matched for age but not reading comprehension ability, were compared on revised versions of the tasks. The pattern of performance of the deaf was different from that of the hearing; they had difficulties when temporal and causal reasoning was text-based, but not when it was nonverbal, indicating that their global temporal/causal reasoning skills are comparable with those of their hearing counterparts
A Quantum Anomaly For Rigid Particles
Canonical quantisation of rigid particles is considered paying special
attention to the restriction on phase space due to causal propagation. A mixed
Lorentz-gravitational anomaly is found in the commutator of Lorentz boosts with
world-line reparametrisations. The subspace of gauge invariant physical states
is therefore not invariant under Lorentz transformations. The analysis applies
for an arbitrary extrinsic curvature dependence with the exception of only one
case to be studied separately. Consequences for rigid strings are also
discussed.Comment: (replaces previous unpritable version corrupted mailer) 12 pages
(Plain TeX), DTP-92/3
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