Farm animal welfare has climbed on the public agenda over the last decades. However, while expectations of public engagement in animal welfare are high, the scholarly literature does not point out any clear dynamic behind public involvement. This article argues that people's trust in institutional actors is a key explanatory factor. Most people's connection to farm animals and farms is indirect and rather To paraphrase, people observe animal welfare through ‘a veil of trust’ in institutions. With dependence on the actions and information from institutional actors, the social dynamics of public concern are closely related to trust in these actors. Trust varies, depending on the social context. Based on a unique integration of Norwegian public opinion surveys conducted from 1997 to 2020, the article analyses how people's concerns and worries over animal welfare are linked to trust in the truth‐telling of institutional actors. The generally high trust in institutions in Norway may therefore explain the continued low levels of public concern for animal welfare. But our analysis also shows that the ‘veil of trust’ in institutions flutters over time and between different categories of actors.

Conditional Independence (CI) testing is fundamental in statistical analysis. For example, CI testing helps validate causal graphs or longitudinal data analysis with repeated measures in causal inference. CI testing is difficult, especially when testing involves categorical variables conditioned on a mixture of continuous and categorical variables. Current parametric and non-parametric testing methods are designed for continuous variables and can quickly fall short in the categorical case. This paper presents a computational approach for CI testing suited for categorical data types, which we call computational conditional independence (CCI) testing. The test procedure is based on permutation and combines machine learning prediction algorithms and Monte Carlo cross-validation. We evaluated the approach through simulation studies and assessed the performance against alternative methods: the generalized covariance measure test, the kernel conditional independence test, and testing with multinomial regression. We find that the computational approach to testing has utility over the alternative methods, achieving better control over type I error rates. We hope this work can expand the toolkit for CI testing for practitioners and researchers.

Cancer mortality has been shown to be associated with social- and human capital. Several channels have been suggested, such as early detection, better compliance to treatment and better health prior to diagnosis. In this paper we study how health status and social capital jointly affect cancer mortality and cancer severity at the time of diagnosis. The analyses are based on study sample of individuals with cancer diagnosis. Our merged dataset contain information on cancer diagnosis and death from the Cancer Registry of Norway and health status, social capital and other individual level data from several national health surveys measured before the time of diagnosis. Health status and social capital are treated as unobserved latent variables, and we apply generalized structural equation modelling framework to estimate conditional statistical associations of social capital and individual health on cancer severity and mortality. We find that health has negative, and statistically significant effect, on cancer mortality, while we cannot conclude on the association between health and cancer severity (metastasis yes/no). We cannot conclude that cancer mortality and the probability of cancer metastasis is associated nor disassociated with social capital. Our results add nuance to prior studies, which frequently report a significant association between social capital and cancer mortality.

Tool for performing computational testing for conditional independence between variables in a dataset. 'CCI' implements permutation in combination with Monte Carlo Cross-Validation in generating null distributions and test statistics. For more details see Computational Test for Conditional Independence (2024) .