Harm Caused by Consumers

The chapter analytically reviews research on the associations between, on the one hand, pornography consumption and, on the other, sexual aggression, attitudes promoting or trivializing violence against women, and sex purchasing. A positive association is found. The complementary methods used to draw causal inferences are illuminated: experiments, naturalistic observations (longitudinal and cross-sectional), and qualitative studies. Mechanisms that explain the effects of nonviolent pornography include subordination and dehumanization of women, targeting of perceived promiscuity, and imitation with unwilling partners. Results are corroborated across studies with samples drawn from the general population, youth, battered women, sex purchasers, and prostituted persons. It is shown how studies that control for variables and moderators such as hostility and promiscuity, which are not independent of the causal variable, likely underestimate pornography’s effects (a problem called post-treatment bias). Additionally, causal overdetermination and other problems in aggregated crime report studies are addressed (e.g., trivialization caused by pornography).

Surprising Causes: Propensity-adjusted Treatment Scores for Multimethod Case Selection

Scholarship on multimethod case selection in the social sciences has developed rapidly in recent years, but many possibilities remain unexplored. This essay introduces an attractive and advantageous new alternative, involving the selection of extreme cases on the treatment variable, net of the statistical influence of the set of known control variables. Cases that are extreme in this way are those in which the value of the main causal variable is as surprising as possible, and thus, this approach can be referred to as seeking “surprising causes.” There are practical advantages to selecting on surprising causes, and there are also advantages in terms of statistical efficiency in facilitating case-study discovery. We first argue for these advantages in general terms and then demonstrate them in an application regarding the dynamics of U.S. labor legislation.

The Marketing Efficiency Development to Create Value-Added for Product and Service of Community-Based Tourism. Study Case for Phatthalung Province

This study aims to examine the hypothesis and to carry out the path analysis of casual relationship influencing the marketing efficiency development to create value-added for product and service of community-based tourism; Phatthalung Province, Thailand. The findings are as follows. First, the hypothesis model matched with the empirical data by considering the value of CMIN/df = 1.596, p-value = .768, GFI = .976, AGFI = 0.972. RMR = 0.000, RMSEA = 0.005, NFI = 0.991, TLI = 0.998 and CFI = 0.999, respectively. The total effect of the causal variable having most effect towards the marketing efficiency development is the entrepreneurial orientation and all compositions explained the marketing efficiency’ s variance accounting for 71.90%. Second, the conceptual model of the causal relationship having effect towards the marketing efficiency development consists of one direct effect variable – the entrepreneurial orientation whereas there are 3 direct and indirect effect variables having effect towards the marketing competency; risk appetite, proactive operation and innovation capability.

Experiments

This chapter discusses experiments. For decades, social scientists were convinced that experimentations were not for them. Consequently, the use of comparative analysis was recommended as a substitute. Yet, since 1990, experiments have become increasingly popular in the social sciences. Experiments have two important advantages compared to observational methods. First, they allow the researcher to clearly identify what the causal variable X is and the outcome Y. Second, with observational methods the precision of the estimates depends on the extent to which the researcher manages to control for the differences between the cases. When the researcher cannot entirely capture these differences, the estimates are likely to be inflated, underestimated, or simply wrong. The chapter then considers the ‘Neyman-Rubin potential-outcome framework’ and looks at the two broad types of experiments: experiments in the field (including survey experiments), and in the lab. It also addresses ethical experiments.

Big data screening identifies 110 complex traits causally associated with obesity

ABSTRACTBackground/ObjectivesObesity has become a serious public health concern worldwide due to the rapid increase in its prevalence and its multiple negative health consequences. Here we sought to identify causal relationships between obesity and other complex traits and conditions using a data-driven hypothesis-free approach that relies on genetic data to infer causal associations.Subjects/MethodsWe leveraged available summary-based genetic data from genome-wide association studies on 1 498 phenotypes and applied the latent causal variable method (LCV) between obesity and all traits.ResultsWe identified 110 traits with significant causal associations with obesity. Results show obesity influencing 26 phenotypes associated with cardiovascular diseases, 22 anthropometric measurements, 9 with the musculoskeletal system, 9 with behavioural or lifestyle factors including loneliness or isolation, 6 with respiratory diseases, 5 with body bioelectric impedances, 4 with psychiatric phenotypes, 4 with the nervous system, 4 with disabilities or long-standing illness, 3 with the gastrointestinal system, 3 with use of analgesics, 2 with metabolic diseases such as diabetes, 1 with inflammatory response and 1 with the neurodevelopmental disorder ADHD, among others.ConclusionsOur results indicate that obesity is primarily the cause, not the consequence of other underlying traits or comorbid diseases. The wide array of causally associated phenotypes provides an overview of the metabolic, physiological, and neuropsychiatric impact of obesity.

Genetic susceptibility to pneumonia: A GWAS meta-analysis between UK Biobank and FinnGen

ABSTRACTRationalePneumonia is a respiratory condition with complex aetiology. Host genetic variation is thought to contribute to individual differences in susceptibility and symptom manifestation.MethodsWe analysed pneumonia data from the UK Biobank (14,780 cases and 439,096 controls) and FinnGen (9,980 cases and 86,519 controls). We perform genome-wide association study (GWAS) meta-analysis, gene-based test, colocalisation, genetic correlation, latent causal variable and polygenic prediction in an independent Australian sample (N=5,595) to draw insights into the genetic aetiology of pneumonia risk.ResultsWe identify two independent loci on chromosome 15 (lead SNPs rs2009746 and rs76474922) to be associated with pneumonia(p<5e-8). Gene-based tests revealed eighteen genes in chromosomes 15,16 and 9, including IL127, PBX3, APOBR and smoking related genes CHRNA3/5, associated with pneumonia. Evidence of HYKK and PBX3 involvement in pneumonia risk was supported by eQTL colocalisation analysis. We observed genetic correlations between pneumonia and cardiorespiratory, psychiatric and inflammatory related traits. Latent causal variable analysis suggests a strong genetic causal relationship cardiovascular health phenotypes and pneumonia risk. Polygenic risk scores (PRS) for pneumonia significantly predicted self-reported pneumonia history in an independent Australian sample, albeit with a small effect size (OR=1.11 95%CI=[1.04-1.19], p<0.05). Sensitivity analyses suggested the associations in chromosome 15 are mediated by smoking history, but the association of genes in chromosome 16 and 9, and polygenic prediction were robust to adjustment for smoking.ConclusionsAltogether, our results highlight common genetic variants, genes and potential pathways that contribute to individual differences in susceptibility to pneumonia, and advance our understanding of the genetic factors underlying heterogeneity in respiratory medical outcomes.

Genetic, lifestyle and environmental risk factors for chronic pain revealed through GWAS

Chronic pain (CP) is a leading cause of disability worldwide with complex aetiologies that remain elusive. Here we addressed this issue by performing a GWAS on a large UK Biobank sample (N=188,352 cases & N=69,627 controls) which identified two independent loci associated with CP near ADAMTS6 and LEMD2. Gene-based tests revealed additional CP-associated genes (DCAKD, NMT1, MLN, IP6K3). Across 1328 complex traits, 548 (41%) were genetically correlated with CP, of which 175 (13%) showed genetic causal relationships using the latent causal variable approach and Mendelian randomization. In particular, major depressive disorder, anxiety, smoking, body fat & BMI were found to increase the risk of CP, whereas diet, walking for pleasure & higher educational attainment were associated with a reduced risk (i.e., protective effect). This data-driven hypothesis-free approach has uncovered several specific risk factors that warrant further examination in longitudinal trials to help deliver effective early screening & management strategies for CP.

Inference of causal relationships between sleep-related traits and 1,527 phenotypes using genetic data

Study ObjectiveSleep is essential for both physical and mental health. There is an increasing interest in understanding how different factors shape individual variation in sleep duration, quality and patterns, or confer risk for sleep disorders. The present study aimed to identify novel causal relationships between sleep-related traits and other phenotypes, using a genetics-driven hypothesis-free approach not requiring longitudinal data.MethodsWe used genetic data and the latent causal variable (LCV) method to screen the phenome and infer causal relationships between seven sleep-related traits (insomnia, daytime dozing, easiness of getting up in the morning, snoring, sleep duration, napping, and morningness) and 1,527 different phenotypes.ResultsWe identify 84 significant causal relationships. Among other findings, poor health of musculoskeletal and connective tissue disorders increase insomnia risk and reduce sleep duration; depression-related traits increase insomnia and daytime dozing; insomnia, napping and snoring are affected by obesity and cardiometabolic traits and diseases; and working with asbestos, thinner, or glues increases insomnia, potentially through an increased risk of respiratory disease.ConclusionOverall, our results indicate that changes in sleep variables are predominantly the consequence, rather than the cause, of other underlying phenotypes and diseases. These insights could inform the design of future epidemiological and interventional studies in sleep medicine and research.