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

Rights-Based Approaches to Development: Office of the High Commissioner for Human Rights

A rights-based approach to development is a conceptual framework for the process of human development that is normatively based on international human rights standards and operationally directed to promoting and protecting human rights. Essentially, a rights-based approach integrates the norms, standards and principles of the international human rights system into the plans, policies and processes of development (The Office of the High Commissioner for Human Rights)

Concussion History and Behavioural Problems in Child and Adolescent Athletes

Sport-related concussion has become a “hot-button” topic in the media and in science. Increasingly, researchers are beginning to understand the association between concussion history and psychosocial adjustment in adults. But little research has been conducted examining this relation in children, and studies done have yielded discrepant results. Therefore, the present study aimed to investigate this relation by using current internalizing and externalizing behaviour to predict past history of concussion among child athletes. Forty-eight (77.1% female) elite community athletes aged 11 to 14 years old (M age = 12.95) completed baseline assessments at the University of Windsor as part of a larger concussion management protocol. Psychosocial functioning was assessed using the parent-report version of the Behavior Assessment System for Children (BASC-3; Internalizing and Externalizing scales only), and previous concussion history was assessed via a demographics questionnaire. A binary logistic regression analysis using the BASC-3 scales as predictors and concussion history (0 vs. ≥1 previous concussion) as the dichotomous outcome variable indicated that the predictors were unable to differentiate between athletes with and without a prior history of concussion. Moreover, neither predictor significantly contributed to the model. These findings suggest that there is no relation between concussion history and current behavioural functioning in this population. Implications of these findings are discussed given the methodological and statistical limitations of the study. Future research should seek to replicate this methodology with more diverse samples to ensure greater generalizability of results

Alternative dispute resolution in Intellectual Property Law: a growing need for a viable alternative to court litigation

Includes abstract.Includes bibliographical references.The need for a viable alternative to court litigation of intellectual property disputes is much needed in modern legal systems. IP court litigation has become expensive, time consuming, and poor decision making has led to unpredictable and inconsistent results. This paper explores the possibility of using alternative methods, such as mediation and arbitration, to resolve complex IP disputes. The paper critiques modern judicial systems and analyses how alternative methods may be better suited to the resolution of IP disputes. Particular attention is paid to the issues present in the South African legal system and what steps are needed to implement a workable and regulated alternative to the High Court system. The paper concludes that alternative dispute mechanisms are well suited to the resolution of IP disputes but that South Africa needs to take progressive steps towards the realisation of such a system

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

Sparse Nested Markov models with Log-linear Parameters

Hidden variables are ubiquitous in practical data analysis, and therefore modeling marginal densities and doing inference with the resulting models is an important problem in statistics, machine learning, and causal inference. Recently, a new type of graphical model, called the nested Markov model, was developed which captures equality constraints found in marginals of directed acyclic graph (DAG) models. Some of these constraints, such as the so called `Verma constraint', strictly generalize conditional independence. To make modeling and inference with nested Markov models practical, it is necessary to limit the number of parameters in the model, while still correctly capturing the constraints in the marginal of a DAG model. Placing such limits is similar in spirit to sparsity methods for undirected graphical models, and regression models. In this paper, we give a log-linear parameterization which allows sparse modeling with nested Markov models. We illustrate the advantages of this parameterization with a simulation study.Comment: Appears in Proceedings of the Twenty-Ninth Conference on Uncertainty in Artificial Intelligence (UAI2013

Insuring the Transatlantic Slave Trade

Copyright © 2019 The Economic History Association. One important, but overlooked, risk mitigation device that facilitated the growth of the slave trade in the eighteenth century was the increasing availability of insurance for ships and their human cargoes. In this article we explore, for the first time, the relative cost of insurance for British slave traders, the underlying processes by which this key aspect of the business of slavery was conducted, and the factors behind price and other changes over time. Comparisons are also drawn with the transatlantic slave trades of other nations. As well as analyzing the business of underwriting slave voyages, we have two other objectives. First, we explore the meaning of slave insurance from the perspective of those directly involved in the trade. Was it about insuring lives or goods? Second, we provide new estimates of the importance of the slave trade to U.K. marine insurance. Did the former drive the growth of the latter, as Joseph Inikori has claimed