The disruption index suffers from citation inflation and is confounded by shifts in scholarly citation practice
颠覆性指数受到引用膨胀与引用实践变化的混淆AI 解读 ↓
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颠覆性指数受到引用膨胀与引用实践变化的混淆AI 解读 ↓
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Measuring the rate of innovation in academia and industry is fundamental to monitoring the efficiency and competitiveness of the knowledge economy. To this end, a disruption index (CD) was recently developed and applied to publication and patent citation networks (Wu et al., Nature 2019; Park et al., Nature 2023). Here we show that CD systematically decreases over time due to secular growth in research and patent production, following two distinct mechanisms unrelated to innovation -- one behavioral and the other structural. Whereas the behavioral explanation reflects shifts associated with techno-social factors (e.g. self-citation practices), the structural explanation follows from `citation inflation' (CI), an inextricable feature of real citation networks attributable to increasing reference list lengths, which causes CD to systematically decrease. We demonstrate this causal link by way of mathematical deduction, computational simulation, multi-variate regression, and quasi-experimental comparison of the disruptiveness of PNAS versus PNAS Plus articles, which differ only in their lengths. Accordingly, we analyze CD data available in the SciSciNet database and find that disruptiveness incrementally increased from 2005-2015, and that the negative relationship between disruption and team-size is remarkably small in overall magnitude effect size, and shifts from negative to positive for team size $\geq$ 8 coauthors.
当处理发生溢出:时空因果效应估计的神经网络框架
Real-world public health policies are often enacted by local jurisdictions, but their effects can ripple through connected populations. For causal evaluation, this creates a difficult spatiotemporal problem: intermittent policy updates create path-level treatments, and because spillover depends on neighboring treatment paths along with the focal-unit's own history, controlling time-varying confounding requires high-dimensional, multimodal information from both sources. To…
时空干扰下用于实验的聚类切换设计
We consider experimentation in the presence of non-stationarity, inter-unit (spatial) interference, and carry-over effects (temporal interference), where we wish to estimate the global average treatment effect (GATE), the difference between average outcomes having exposed all units at all times to treatment or to control. We suppose spatial interference is described by a graph, where a unit's outcome depends on its neighborhood's treatments, and that temporal interference is…
社会网络数据中处理效应的识别与估计:当社会连接存在混杂时
In observational network data, the adoption of a treatment and the formation of social ties often depend on the same characteristics that affect outcomes. We propose a generalized propensity score for each unit's effective treatment, built from a model for treatment assignment and a dyadic model for network formation, which may include latent node heterogeneity. The score is marginalized over the network given covariates rather than conditioned on the realized network, and…
复杂(准)实验设计下的简单标准误
We develop standard errors for estimates of causal effects in settings where the distribution of treatment exposures is known but potentially exhibits complicated dependence patterns across units. Such dependence arises, for example, in settings with spillovers/interference across units or when the treatment assignment is spatially correlated. Our variance estimator generalizes several properties of the Neyman variance estimator for completely randomized experiments to…
边处理与节点结果的网络实验
We present a methodology for analyzing node-level outcomes while experimenting with edge-level treatments in a population connected by an undirected graph. Under our design, nodes are randomly assigned to test or control, and each edge inherits the treatment of its endpoints, with conflicts resolved by randomization. We use each node's assigned status as an instrument for its treatment exposure. We show that the Wald estimator is consistent for the global average treatment…
Comparing dependence structures between two related multivariate time series is of fundamental interest in many scientific applications, where changes may occur in both directed temporal interactions and contemporaneous connectivity. Modeling each time series by a vector autoregressive (VAR) model, we propose a new framework for estimation and inference of differential Granger causality (DiffGC) and differential network (DiffNet) structures in high dimensions. The proposed…