Causal inference on networks under continuous treatment interference

This paper investigates the case of interference, when a unit’s treatment also affects other units’ outcome. When interference is at work, policy evaluation mostly relies on the use of randomized experiments under cluster interference and binary treatment. Instead, we consider a non-experimental setting under continuous treatment and network interference. In particular, we define spillover effects by specifying the exposure to network treatment as a weighted average of the treatment received by units connected through physical, social or economic interactions. Building on Forastiere et al. (2021), we provide a generalized propensity score-based estimator to estimate both direct and spillover effects of a continuous treatment. Our estimator also allows to consider asymmetric network connections characterized by heterogeneous intensities. To showcase this methodology, we investigate whether and how spillover effects shape the optimal level of policy interventions in agricultural markets. Our results show that, in this context, neglecting interference may underestimate the degree of policy effectiveness.     

计算机网络中的著作权保护

数字技术的发展,导致数字编码形式作品的出现.近些年来互联网和社会信息化的迅速发展又强烈刺激了这种作品的广泛传播.数字编码形式作品的出现以及互联网的发展,强烈地影响了作品的复制、出版、创作、传播过程,进而对现行作品版权保护制度提出了一系列新问题,形成了对现行版权保护制度的猛烈冲击,怎样妥善处理这一系列新问题已经成为国际知识产权界共同关心的热点,也必然是我国当前正在进行的<著作权法>修订工作内容的重点之一.     

A Multiple Objective Embedded Network Model for Human Resource Planning and an Implementation of the Tchebycheff Method

A multiple objective embedded network model is proposed to model a variety of human resource planning problems including executive succession planning, compensation planning, training program design, diversity management and human systems design. The Tchebycheff Method, an interactive multiple objective programming solution procedure developed by Steuer and Choo [32], is implemented using NETSIDE, a computer routine for solving network problems with side constraints developed by Kennington and Whisman [17]. This paper demonstrates how the network structure common to many types of human resource planning problems can be exploited to improve solution efficiency, and how our approach extends the use of network models in human resource planning by including multiple objectives and extranetwork constraints. An illustrative example demonstrating the modeling and solution approach is presented, and the potential applications of these approaches in two specific areas of human resource planning are discussed.