7,452 research outputs found
Estimating Causal Installed-Base Effects: A Bias-Correction Approach
New empirical models of consumer demand that incorporate social
preferences, observational learning, word-of-mouth or network effects
have the feature that the adoption of others in the reference group -
the “installed-base” - has a causal effect on current
adoption behavior. Estimation of such causal installed-base effects is
challenging due to the potential for spurious correlation between the
adoption of agents, arising from endogenous assortive matching into
social groups (or homophily) and from the existence of unobservables
across agents that are correlated. In the absence of experimental
variation, the preferred solution is to control for these using a rich
specification of fixed-effects, which is feasible with panel data. We
show that fixedeffects estimators of this sort are inconsistent in the
presence of installed-base effects; in our simulations, random-effects
specifications perform even worse. Our analysis reveals the tension
faced by the applied empiricist in this area: a rich control for
unobservables increases the credibility of the reported causal effects,
but the incorporation of these controls introduces biases of a new kind
in this class of models. We present two solutions: an instrumental
variable approach, and a new bias-correction approach, both of which
deliver consistent estimates of causal installed-base effects. The
bias-correction approach is tractable in this context because we are
able to exploit the structure of the problem to solve analytically for
the asymptotic bias of the installed-base estimator, and to incorporate
it into the estimation routine. Our approach has implications for the
measurement of social effects using non-experimental data, and for
measuring marketing-mix effects in the presence of state-dependence in
demand, more generally. Our empirical application to the adoption of the
Toyota Prius Hybrid in California reveals evidence for social influence
in diffusion, and demonstrates the importance of incorporating proper
controls for the biases we identify
Digital Image
This paper considers the ontological significance of invisibility in relation to the question ‘what is a digital image?’ Its argument in a nutshell is that the emphasis on visibility comes at the expense of latency and is symptomatic of the style of thinking that dominated Western philosophy since Plato. This privileging of visible content necessarily binds images to linguistic (semiotic and structuralist) paradigms of interpretation which promote representation, subjectivity, identity and negation over multiplicity, indeterminacy and affect. Photography is the case in point because until recently critical approaches to photography had one thing in common: they all shared in the implicit and incontrovertible understanding that photographs are a medium that must be approached visually; they took it as a given that photographs are there to be looked at and they all agreed that it is only through the practices of spectatorship that the secrets of the image can be unlocked. Whatever subsequent interpretations followed, the priori- ty of vision in relation to the image remained unperturbed. This undisputed belief in the visibility of the image has such a strong grasp on theory that it imperceptibly bonded together otherwise dissimilar and sometimes contradictory methodol- ogies, preventing them from noticing that which is the most unexplained about images: the precedence of looking itself. This self-evident truth of visibility casts a long shadow on im- age theory because it blocks the possibility of inquiring after everything that is invisible, latent and hidden
Weighted-Lasso for Structured Network Inference from Time Course Data
We present a weighted-Lasso method to infer the parameters of a first-order
vector auto-regressive model that describes time course expression data
generated by directed gene-to-gene regulation networks. These networks are
assumed to own a prior internal structure of connectivity which drives the
inference method. This prior structure can be either derived from prior
biological knowledge or inferred by the method itself. We illustrate the
performance of this structure-based penalization both on synthetic data and on
two canonical regulatory networks, first yeast cell cycle regulation network by
analyzing Spellman et al's dataset and second E. coli S.O.S. DNA repair network
by analysing U. Alon's lab data
Multiscale sampling model for motion integration
Biologically plausible strategies for visual scene integration across spatial and temporal domains continues to be a challenging topic. The fundamental question we address is whether classical problems in motion integration, such as the aperture problem, can be solved in a model that samples the visual scene at multiple spatial and temporal scales in parallel. We hypothesize that fast interareal connections that allow feedback of information between cortical layers are the key processes that disambiguate motion direction. We developed a neural model showing how the aperture problem can be solved using different spatial sampling scales between LGN, V1 layer 4, V1 layer 6, and area MT. Our results suggest that multiscale sampling, rather than feedback explicitly, is the key process that gives rise to end-stopped cells in V1 and enables area MT to solve the aperture problem without the need for calculating intersecting constraints or crafting intricate patterns of spatiotemporal receptive fields. Furthermore, the model explains why end-stopped cells no longer emerge in the absence of V1 layer 6 activity (Bolz & Gilbert, 1986), why V1 layer 4 cells are significantly more end-stopped than V1 layer 6 cells (Pack, Livingstone, Duffy, & Born, 2003), and how it is possible to have a solution to the aperture problem in area MT with no solution in V1 in the presence of driving feedback. In summary, while much research in the field focuses on how a laminar architecture can give rise to complicated spatiotemporal receptive fields to solve problems in the motion domain, we show that one can reframe motion integration as an emergent property of multiscale sampling achieved concurrently within lamina and across multiple visual areas.This work was supported in part by CELEST, a National Science Foundation Science of Learning Center; NSF SBE-0354378 and OMA-0835976; ONR (N00014-11-1-0535); and AFOSR (FA9550-12-1-0436). (CELEST, a National Science Foundation Science of Learning Center; SBE-0354378 - NSF; OMA-0835976 - NSF; N00014-11-1-0535 - ONR; FA9550-12-1-0436 - AFOSR)Published versio
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