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

Interleukin-17 is required for control of chronic lung infection caused by Pseudomonas aeruginosa

Chronic pulmonary infection with Pseudomonas aeruginosa is a feature of cystic fibrosis (CF) and other chronic lung diseases. Cytokines of the IL-17 family have been proposed as important in the host response to P. aeruginosa infection through augmenting antibacterial immune responses, although their pro-inflammatory effect may contribute to lung damage that occurs as a result of chronic infection. We set out to explore the role of IL-17 in the host response to chronic P. aeruginosa infection. We used a murine model of chronic pulmonary infection with CF-related strains of P. aeruginosa. We demonstrate that IL-17 cytokine signaling is essential for survival and prevention of chronic infection at 2 weeks post-inoculation using two different P. aeruginosa strains. Following infection, there was a marked expansion of cells within mediastinal lymph nodes, comprised mainly of innate lymphoid cells (ILCs); ∼90% of IL-17 producing cells had markers consistent with Group 3 ILCs. A smaller percentage of IL-17+ cells had markers consistent with a B1 phenotype. In lung homogenates 14 days following infection, there was a significant expansion of IL-17+ cells – about 50% of these were CD3+, split equally between CD4+ Th17 cells and γδ T cells, while the CD3- IL-17+ cells were almost exclusively Group 3 ILCs. Further experiments with B cell deficient mice showed that B cell production of IL-17 or natural antibodies did not provide any defence against chronic P. aeruginosa infection. Thus, IL-17 rather than antibody is a key element in host defence against chronic pulmonary infection with P. aeruginosa

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

'Finding beauty' in French rural prisons. How prison officers operate rurality

The literature on rural criminology and rural prisons has so far essentially focused on debunking myths about rurality and rural crimes, and on the economic and social impacts of building prisons in rural areas. Typically, such rural prisons are recent. Conversely, due to its long history, France's rural prisons have in some cases been built during the 19th century within former convents from the Middle Ages or monasteries confiscated from the church during the 1789 Revolution. Missing from this literature, therefore, is, on the one hand, a focus on historic rural prison settings and, on the other hand, attention to individuals and professionals who work there. This paper focuses on a high security prison set in a middle-ages abbey in the middle of nature. In our interviews with its prison officers (POs) we used appreciative inquiry in order to better uncover the positive dimensions of rurality. We find that rurality is used to reinforce safety and the 'right distance' with prisoners, and to better cut off from the prison environment when they finish their shift. We also find that POs are bound by strong (rural) family ties that in turn contribute to their professional identity and values, and to their feelings of safety