نتایج جستجو برای: causal process
تعداد نتایج: 1366962 فیلتر نتایج به سال:
An encoder causally observes the Wiener process and decides when what to transmit about it. A decoder makes real-time estimation of using received codewords. We determine causal encoding decoding policies that minimize mean-square error, under long-term communication rate constraint R bits/s. show an optimal policy can be implemented as a sampling followed by compressing policy. prove samples o...
To make causal inferences from observational data, researchers have often turned to matching methods. These methods are variably successful. We address issues with matching methods by redefining the matching problem as a subset selection problem. Given a set of covariates, we seek to find two subsets, a control group and a treatment group, so that we obtain optimal balance, or, in other words, ...
A new sufficient condition for the existence of a stationary causal solution of an ARCH(∞) equation is provided. This condition allows to consider coefficients with power-law decay, so that it can be applied to the so-called FIGARCH processes, whose existence is thus proved.
Established guidelines for causal inference in epidemiological studies may be inappropriate for genetic associations. A consensus process was used to develop guidance criteria for assessing cumulative epidemiologic evidence in genetic associations. A proposed semi-quantitative index assigns three levels for the amount of evidence, extent of replication, and protection from bias, and also genera...
A fundamental goal in network neuroscience is to understand how activity in one brain region drives activity elsewhere, a process referred to as effective connectivity. Here we propose to model this causal interaction using integro-differential equations and causal kernels that allow for a rich analysis of effective connectivity. The approach combines the tractability and flexibility of autoreg...
Complex systems can be modelled at various levels of detail. Ideally, causal models of the same system should be consistent with one another in the sense that they agree in their predictions of the effects of interventions. We formalise this notion of consistency in the case of Structural Equation Models (SEMs) by introducing exact transformations between SEMs. This provides a general language ...
Although the financial services industry is a large buyer of outsourcing services, it still lags behind other industries especially regarding business process outsourcing (BPO). This research in progress asks why. The main hypothesis is that subjectively perceived risk is decisive for senior management's attitude towards BPO. A causal model will be developed, derived from Perceived Risk Theory ...
Temporal information plays a major role in human causal inference. We present a rational framework for causal induction from events that take place in continuous time. We define a set of desiderata for such a framework and outline a strategy for satisfying these desiderata using continuous-time stochastic processes. We develop two specific models within this framework, illustrating how it can b...
Cyclic Causal Models with Discrete Variables: Markov Chain Equilibrium Semantics and Sample Ordering
We analyze the foundations of cyclic causal models for discrete variables, and compare structural equation models (SEMs) to an alternative semantics as the equilibrium (stationary) distribution of a Markov chain. We show under general conditions, discrete cyclic SEMs cannot have independent noise; even in the simplest case, cyclic structural equation models imply constraints on the noise. We gi...
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