Tea can improve the progression of some metabolic diseases through anti-inflammatory and antioxidant effects, but its impact on non-alcoholic fatty liver disease (NAFLD) is still controversial. The aim of this paper is to identify the relationship between tea and NAFLD by Mendelian randomisation (MR) and complete clinical validation using National Health and Nutrition Examination Survey (NHANES) database. MR used data from Genome Wide Association Study, with inverse-variance weighted (IVW) as principal analytical methods. The reliability of the results was verified by a series of sensitivity and heterogeneity tests. Subsequently, clinical validation was conducted using NHANES (2005–2018), involving 22 257 participants, grouped by the type of tea. Green tea drinkers were categorised into four groups (Q1–Q4) by quartiles of green tea intake, from lowest to highest (similar for black tea drinkers and other tea drinkers). Models were constructed by logistic regression to estimate the role of tea consumption (Q1–4) on NAFLD. Finally, using fibrosis-4 index (FIB-4) to evaluate the severity of hepatic fibrosis, the effect of tea consumption (Q1–4) on the degree of hepatic fibrosis was investigated by linear regression. IVW method (OR = 0·43, 95 % CI: 0·21, 0·85, P = 0·01) and weighted median method (OR = 0·35, 95 % CI: 0·14, 0·91, P = 0·03) revealed there was a causal relationship between tea and NAFLD. An array of sensitivity analyses validated the reliability of results. Analysis of NHANES indicated tea drinker present a slightly lower prevalence of NAFLD than non-tea drinker (green tea drinkers: 47·6 %, black tea drinkers: 46·3 %, other tea drinker: 43·2 %, non-tea drinkers: 48·1 %, P < 0·05). After adjusting for confounders, compared with the lowest black tea consumption (Q1), the population with the highest black tea consumption (Q4) was independently related to lower presence of NAFLD (Q4: OR = 0·69, 95 % CI: 0·50, 0·93, P < 0·05), such association remained stable in the overweight subgroup. As further analysed, Q4 also displayed a significant negative correlation with the level of hepatic fibrosis in patients with NAFLD (β = –0·073, 95 % CI: –0·126, −0·020, P < 0·01).Tea reduces the morbidity of NAFLD and ameliorates hepatic fibrosis degree in those already suffering from the disease.

The expensive-tissue hypothesis (ETH) posited a brain–gut trade-off to explain how humans evolved large, costly brains. Versions of the ETH interrogating gut or other body tissues have been tested in non-human animals, but not humans. We collected brain and body composition data in 70 South Asian women and used structural equation modelling with instrumental variables, an approach that handles threats to causal inference including measurement error, unmeasured confounding and reverse causality. We tested a negative, causal effect of the latent construct ‘nutritional investment in brain tissues’ (MRI-derived brain volumes) on the construct ‘nutritional investment in lean body tissues’ (organ volume and skeletal muscle). We also predicted a negative causal effect of the brain latent on fat mass. We found negative causal estimates for both brain and lean tissue (−0.41, 95% CI, −1.13, 0.23) and brain and fat (−0.56, 95% CI, −2.46, 2.28). These results, although inconclusive, are consistent with theory and prior evidence of the brain trading off with lean and fat tissues, and they are an important step in assessing empirical evidence for the ETH in humans. Analyses using larger datasets, genetic data and causal modelling are required to build on these findings and expand the evidence base.

Instrumental variable (IV) strategies are widely used in political science to establish causal relationships, but the identifying assumptions required by an IV design are demanding, and assessing their validity remains challenging. In this paper, we replicate 67 articles published in three top political science journals from 2010 to 2022 and identify several concerning patterns. First, researchers often overestimate the strength of their instruments due to non-i.i.d. error structures such as clustering. Second, IV estimates are often highly uncertain, and the commonly used t-test for two-stage-least-squares (2SLS) estimates frequently underestimate the uncertainties. Third, in most replicated studies, 2SLS estimates are significantly larger in magnitude than ordinary-least-squares estimates, and their absolute ratio is inversely related to the strength of the instrument in observational studies—a pattern not observed in experimental ones—suggesting potential violations of unconfoundedness or the exclusion restriction in the former. We provide a checklist and software to help researchers avoid these pitfalls and improve their practice.

Mendelian randomization (MR) leverages genetic information to examine the causal relationship between phenotypes allowing for the presence of unmeasured confounders. MR has been widely applied to unresolved questions in epidemiology, making use of summary statistics from genome-wide association studies on an increasing number of human traits. However, an understanding of essential concepts is necessary for the appropriate application and interpretation of MR. This review aims to provide a non-technical overview of MR and demonstrate its relevance to psychiatric research. We begin with the origins of MR and the reasons for its recent expansion, followed by an overview of its statistical methodology. We then describe the limitations of MR, and how these are being addressed by recent methodological advances. We showcase the practical use of MR in psychiatry through three illustrative examples – the connection between cannabis use and psychosis, the link between intelligence and schizophrenia, and the search for modifiable risk factors for depression. The review concludes with a discussion of the prospects of MR, focusing on the integration of multi-omics data and its extension to delineating complex causal networks.

This study aimed to investigate the association between n-3 PUFA and lung function. First, a cross-sectional study was conducted based on the National Health and Nutrition Examination Survey (NHANES) 2007–2012 data. n-3 PUFA intake was obtained from 24-h dietary recalls. A multivariable linear regression model was used to assess the observational associations of n-3 PUFA intake with lung function. Subsequently, a two-sample Mendelian randomisation (MR) was performed to estimate the potential causal effect of n-3 PUFA on lung function. Genetic instrumental variables were extracted from published genome-wide association studies. Summary statistics about n-3 PUFA was from UK Biobank. Inverse variance weighted was the primary analysis approach. The observational study did not demonstrate a significant association between n-3 PUFA intake and most lung function measures; however, a notable exception was observed with significant findings in the highest quartile for forced vital capacity (FVC) and % predicted FVC. The MR results also showed no causal effect of circulating n-3 PUFA concentration on lung function (forced expiratory volume in one second (FEV1), β = 0·01301, se = 0·01932, P = 0·5006; FVC, β = −0·001894, se = 0·01704, P = 0·9115; FEV1:FVC, β = 0·03118, se = 0·01743, P = 0·07359). These findings indicate the need for further investigation into the impact of higher n-3 PUFA consumption on lung health.

Is the quality of democracy undermined or enhanced by party-system fragmentation? Addressing this question would help us better assess normative claims about electoral reforms. Yet, doing so is difficult because of endogeneity issues: party systems are endogenous to many other dynamics in a polity. We overcome this problem by putting forward an instrument for the number of parties in a system, based on the level of fragmentation added by parties that narrowly make it to parliament. We then test the effect of party-system fragmentation on the quality of democracy, drawing upon an extensive battery of outcomes. Against previous literature, we find that a higher number of parties leads to more fractionalized governments, but has no impact on other democratic outcomes. Subsample analyses suggest that fragmentation may have some effect in contexts of very high polarization, but we find no effect in other theoretically meaningful subsamples. Our results indicate that party-system fragmentation may have fewer normative implications than previously assumed.

We combined smartphone mobility data with census track-based reports of positive case counts to study a coronavirus disease 2019 (COVID-19) outbreak at the University of Wisconsin–Madison campus, where nearly 3000 students had become infected by the end of September 2020. We identified a cluster of twenty bars located at the epicentre of the outbreak, in close proximity to campus residence halls. Smartphones originating from the two hardest-hit residence halls (Sellery-Witte), where about one in five students were infected, were 2.95 times more likely to visit the 20-bar cluster than smartphones originating in two more distant, less affected residence halls (Ogg-Smith). By contrast, smartphones from Sellery-Witte were only 1.55 times more likely than those from Ogg-Smith to visit a group of 68 restaurants in the same area [rate ratio 1.91, 95% confidence interval (CI) 1.29–2.85, P < 0.001]. We also determined the per-capita rates of visitation to the 20-bar cluster and to the 68-restaurant comparison group by smartphones originating in each of 21 census tracts in the university area. In a multivariate instrumental variables regression, the visitation rate to the bar cluster was a significant determinant of the per-capita incidence of positive severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) tests in each census tract (elasticity 0.88, 95% CI 0.08–1.68, P = 0.032), while the restaurant visitation rate showed no such relationship. The potential super-spreader effects of clusters or networks of places, rather than individual sites, require further attention.

The international community often seeks to promote political reforms in recalcitrant states. Recently, some scholars have argued that, rather than helping, international law and advocacy create new problems because they have negative spillovers that increase rights violations. We review three mechanisms for such spillovers: backlash, trade-offs, and counteraction and concentrate on the last of these. Some researchers assert that governments sometimes “counteract” international human rights pressures by strategically substituting violations in adjacent areas that are either not targeted or are harder to monitor. However, most such research shows only that both outcomes correlate with an intervention—the targeted positively and the spillover negatively. The burden of proof, however, should be as rigorous as those for studies of first-order policy consequences. We show that these correlations by themselves are insufficient to demonstrate counteraction outside of the narrow case where the intervention is assumed to have no direct effect on the spillover, a situation akin to having a valid instrumental variable design. We revisit two prominent findings and show that the evidence for the counteraction claim is weak in both cases. The article contributes methodologically to the study of negative spillovers in general by proposing mediation and sensitivity analysis within an instrumental variables framework for assessing such arguments. It revisits important prior findings that claim negative consequences to human rights law and/or advocacy, and raises critical normative questions regarding how we empirically evaluate hypotheses about causal mechanisms.

In text, images, merged surveys, voter files, and elsewhere, data sets are often missing important covariates, either because they are latent features of observations (such as sentiment in text) or because they are not collected (such as race in voter files). One promising approach for coping with this missing data is to find the true values of the missing covariates for a subset of the observations and then train a machine learning algorithm to predict the values of those covariates for the rest. However, plugging in these predictions without regard for prediction error renders regression analyses biased, inconsistent, and overconfident. We characterize the severity of the problem posed by prediction error, describe a procedure to avoid these inconsistencies under comparatively general assumptions, and demonstrate the performance of our estimators through simulations and a study of hostile political dialogue on the Internet. We provide software implementing our approach.

This article has used the method of instrumental variables to evaluate the impact of health services on the productivity of rural households’ farming labor in Burkina Faso. The distance from the household's homestead to the Health and Social Promotion Center (HSPC) was considered as an instrumental variable. The results revealed that resorting to a HSPC in case of an unexpected illness in the rainy season significantly improves the farm labor productivity by FCFA 3170.5880 per person-day. For improving agricultural productivity, we suggest that public decision-makers should focus on the availability and the quality of HSPC services in rural areas.

Instrumental-variable (IV) estimation is an essential method for applied researchers across the social and behavioral sciences who analyze randomized control trials marred by noncompliance or leverage partially exogenous treatment variation in observational studies. The potential outcome framework is a popular model to motivate the assumptions underlying the identification of the local average treatment effect (LATE) and to stratify the sample into compliers, always-takers, and never-takers. However, applied research has thus far paid little attention to the characteristics of compliers and noncompliers. Yet, profiling compliers and noncompliers is necessary to understand what subpopulation the researcher is making inferences about and an important first step in evaluating the external validity (or lack thereof) of the LATE estimated for compliers. In this letter, we discuss the assumptions necessary for profiling, which are weaker than the assumptions necessary for identifying the LATE if the instrument is randomly assigned. We introduce a simple and general method to characterize compliers, always-takers, and never-takers in terms of their covariates and provide easy-to-use software in R and STATA that implements our estimator. We hope that our method and software facilitate the profiling of compliers and noncompliers as a standard practice accompanying any IV analysis.

Does exposure to the refugee crisis fuel support for extreme-right parties? Despite heated debates about the political repercussions of the refugee crisis in Europe, there exists very little—and sometimes conflicting—evidence with which to assess the impact of a large influx of refugees on natives’ political attitudes and behavior. We provide causal evidence from a natural experiment in Greece, where some Aegean islands close to the Turkish border experienced sudden and drastic increases in the number of Syrian refugees while other islands slightly farther away—but with otherwise similar institutional and socioeconomic characteristics—did not. Placebo tests suggest that precrisis trends in vote shares for exposed and nonexposed islands were virtually identical. This allows us to obtain unbiased estimates of the electoral consequences of the refugee crisis. Our study shows that among islands that faced a massive but transient inflow of refugees passing through just before the September 2015 election, vote shares for Golden Dawn, the most extreme-right party in Europe, moderately increased by 2 percentage points (a 44 percent increase at the average). The finding that mere exposure to the refugee crisis is sufficient to fuel support for extreme-right parties has important implications for our theoretical understanding of the drivers of antirefugee backlash.

This paper introduces an instrumental variables framework for analyzing how external factors that affect survey response rates can also affect the composition of the sample of respondents. The method may be useful for studying survey representativeness, and for assessing the effectiveness of some of the conventional corrections for survey nonresponse bias.

The paper applies the method to data collected in the 2011 Swiss Electoral Study (SES), in which survey participation incentives were randomly assigned across members of the original survey sample. The empirical analysis shows that the incentives increased response rates substantially. Estimates of a new instrumental variable parameter called the Complier Average Survey Response (CASR) suggest that the incentives induced participation among people with more nationalist political opinions than those who would have participated without the incentives. Weighting the respondent data to match the covariate distribution in the target population did not account for the discrepancy in attitudes between the two groups, suggesting that the weights would not succeed in removing nonresponse bias.