Inference under constrained distribution shifts

Large-scale administrative or observational datasets are increasingly used to inform decision making. While this effort aims to ground policy in real-world evidence, challenges have arise as that selection bias and other forms of distribution shift often plague observational data. Previous attempts to provide robust inferences have given guarantees depending on a user-specified amount of possible distribution shift (e.g., the maximum KL divergence between the observed and target distributions). However, decision makers will often have additional knowledge about the target distribution which constrains the kind of shifts which are possible. To leverage such information, we proposed a framework that enables statistical inference in the presence of distribution shifts which obey user-specified constraints in the form of functions whose expectation is known under the target distribution. The output is high-probability bounds on the value an estimand takes on the target distribution. Hence, our method leverages domain knowledge in order to partially identify a wide class of estimands. We analyze the computational and statistical properties of methods to estimate these bounds, and show that our method can produce informative bounds on a variety of simulated and semisynthetic tasks

Auditing Fairness by Betting

We provide practical, efficient, and nonparametric methods for auditing the fairness of deployed classification and regression models. Whereas previous work relies on a fixed-sample size, our methods are sequential and allow for the continuous monitoring of incoming data, making them highly amenable to tracking the fairness of real-world systems. We also allow the data to be collected by a probabilistic policy as opposed to sampled uniformly from the population. This enables auditing to be conducted on data gathered for another purpose. Moreover, this policy may change over time and different policies may be used on different subpopulations. Finally, our methods can handle distribution shift resulting from either changes to the model or changes in the underlying population. Our approach is based on recent progress in anytime-valid inference and game-theoretic statistics-the "testing by betting" framework in particular. These connections ensure that our methods are interpretable, fast, and easy to implement. We demonstrate the efficacy of our methods on several benchmark fairness datasets.Comment: 24 pages, 4 figure

Winning on Climate Change: How Philanthropy Can Spur Major Progress over the Next Decade

Over the next 10 years, major progress against climate change is entirely possible, and philanthropy has an important role to play. Through interviews with experts and building on previous work with actors in the field, this report identifies three climate philanthropy practices that will be especially important in the decade ahead.

Testing for Network and Spatial Autocorrelation

Testing for dependence has been a well-established component of spatial statistical analyses for decades. In particular, several popular test statistics have desirable properties for testing for the presence of spatial autocorrelation in continuous variables. In this paper we propose two contributions to the literature on tests for autocorrelation. First, we propose a new test for autocorrelation in categorical variables. While some methods currently exist for assessing spatial autocorrelation in categorical variables, the most popular method is unwieldy, somewhat ad hoc, and fails to provide grounds for a single omnibus test. Second, we discuss the importance of testing for autocorrelation in network, rather than spatial, data, motivated by applications in social network data. We demonstrate that existing tests for autocorrelation in spatial data for continuous variables and our new test for categorical variables can both be used in the network setting

Supermercado exclusivo para productos lácteos “Nicamilk”, ubicado en el municipio de Diriamba, departamento de Carazo, durante el primer y segundo semestre del año 2022

El presente estudio en desarrollo se divide en 3 partes fundamentales; Como primera instancia se muestran los aspectos relacionados con el establecimiento de un supermercado exclusivo para la comercialización de productos lácteos derivados del ganado vacuno y caprino en el departamento de Carazo, municipio de Diriamba durante el periodo 2023-2028, así bien ofrecer platillos para aquellos que deseen de gustar de los productos de manera directa a su vez ofreciendo servicios de delivery. En base a lo anterior, se ha realizado un estudio de mercado en donde la metodología aplicada fue la exploración de campo, utilizando técnicas cuantitativas y cualitativas de predicción como encuestas y entrevista, determinando así la demanda de los productos, la demanda real y potencial para la comercialización de lácteos, de igual forma se ha determinado los costos y precios para la venta de manera directa, al mismo tiempo que se presentan los distintos proveedores de materia prima para el supermercado. En segunda Instancia, se ha efectuado un estudio técnico el cual se enfocó en la construcción de las instalaciones de la entidad, se estableció una macro y micro localización de la entidad, se realizaron medidas de las tuberías de agua potable, aguas residuales y sistema eléctrico, además se detectaron y se cotizaron losprecios de los distintos equipos y mobiliarios a utilizar para la comercialización de los producto con sus respectivos costos y cantidades, también se concreta la ubicación y distribución de las áreas del supermercado. El tercer paso es uno de las partes más importantes del proyecto en el cual se realiza un estudio financiero para determinar la viabilidad y rentabilidad mediante la utilización de las distintas razones financieras, en donde se proyecta descubrir el beneficio a obtener una vez que se tome la decisión de invertir en el establecimiento del supermercado a través del punto retorno a la inversión (ROI), así bien estimar el monto de inversión necesario para poner en marcha el proyecto

Constraining primordial non-Gaussianity with future galaxy surveys

We study the constraining power on primordial non-Gaussianity of future surveys of the large-scale structure of the Universe for both near-term surveys (such as the Dark Energy Survey - DES) as well as longer term projects such as Euclid and WFIRST. Specifically we perform a Fisher matrix analysis forecast for such surveys, using DES-like and Euclid-like configurations as examples, and take account of any expected photometric and spectroscopic data. We focus on two-point statistics and we consider three observables: the 3D galaxy power spectrum in redshift space, the angular galaxy power spectrum, and the projected weak-lensing shear power spectrum. We study the effects of adding a few extra parameters to the basic LCDM set. We include the two standard parameters to model the current value for the dark energy equation of state and its time derivative, w_0, w_a, and we account for the possibility of primordial non-Gaussianity of the local, equilateral and orthogonal types, of parameter fNL and, optionally, of spectral index n_fNL. We present forecasted constraints on these parameters using the different observational probes. We show that accounting for models that include primordial non-Gaussianity does not degrade the constraint on the standard LCDM set nor on the dark-energy equation of state. By combining the weak lensing data and the information on projected galaxy clustering, consistently including all two-point functions and their covariance, we find forecasted marginalised errors sigma (fNL) ~ 3, sigma (n_fNL) ~ 0.12 from a Euclid-like survey for the local shape of primordial non-Gaussianity, while the orthogonal and equilateral constraints are weakened for the galaxy clustering case, due to the weaker scale-dependence of the bias. In the lensing case, the constraints remain instead similar in all configurations.Comment: 20 pages, 10 Figures. Minor modifications; accepted by MNRA

Food allergy: recent advances in pathophysiology and treatment

Food allergies are adverse immune reactions to food proteins that affect up to 6% of children and 3-4% of adults. A wide range of symptoms can occur depending on whether IgE or non-IgE mediated mechanism are involved. Many factors influence the development of oral tolerance, including route of exposure, genetics, age of the host, and allergen factors. Advances have been made in the understanding of how these factors interact in the pathophysiology of food allergy. Currently, the mainstay of treatment for food allergies is avoidance and ready access to emergency medications. However, with the improved understanding of tolerance and advances in characterization of food allergens, several therapeutic strategies have been developed and are currently being investigated as potential treatments and/or cures for food allergy

The Monarch Initiative in 2024: an analytic platform integrating phenotypes, genes and diseases across species.

Bridging the gap between genetic variations, environmental determinants, and phenotypic outcomes is critical for supporting clinical diagnosis and understanding mechanisms of diseases. It requires integrating open data at a global scale. The Monarch Initiative advances these goals by developing open ontologies, semantic data models, and knowledge graphs for translational research. The Monarch App is an integrated platform combining data about genes, phenotypes, and diseases across species. Monarch\u27s APIs enable access to carefully curated datasets and advanced analysis tools that support the understanding and diagnosis of disease for diverse applications such as variant prioritization, deep phenotyping, and patient profile-matching. We have migrated our system into a scalable, cloud-based infrastructure; simplified Monarch\u27s data ingestion and knowledge graph integration systems; enhanced data mapping and integration standards; and developed a new user interface with novel search and graph navigation features. Furthermore, we advanced Monarch\u27s analytic tools by developing a customized plugin for OpenAI\u27s ChatGPT to increase the reliability of its responses about phenotypic data, allowing us to interrogate the knowledge in the Monarch graph using state-of-the-art Large Language Models. The resources of the Monarch Initiative can be found at monarchinitiative.org and its corresponding code repository at github.com/monarch-initiative/monarch-app