Causal Discovery with Confounding Cascade Nonlinear Additive Noise Models

Identification of causal direction between a causal-effect pair from observed data has recently attracted much attention. Various methods based on functional causal models have been proposed to solve this problem, by assuming the causal process satisfies some (structural) constraints and showing that the reverse direction violates such constraints. The nonlinear additive noise model has been demonstrated to be effective for this purpose, but the model class does not allow any confounding or intermediate variables between a cause pair–even if each direct causal relation follows this model. However, omitting the latent causal variables is frequently encountered in practice. After the omission, the model does not necessarily follow the model constraints. As a consequence, the nonlinear additive noise model may fail to correctly discover causal direction. In this work, we propose a confounding cascade nonlinear additive noise model to represent such causal influences–each direct causal relation follows the nonlinear additive noise model but we observe only the initial cause and final effect. We further propose a method to estimate the model, including the unmeasured confounding and intermediate variables, from data under the variational auto-encoder framework. Our theoretical results show that with our model, the causal direction is identifiable under suitable technical conditions on the data generation process. Simulation results illustrate the power of the proposed method in identifying indirect causal relations across various settings, and experimental results on real data suggest that the proposed model and method greatly extend the applicability of causal discovery based on functional causal models in nonlinear cases.

Codon-specific Ramachandran plots show amino acid backbone conformation depends on identity of the translated codon.

Synonymous codons translate into chemically identical amino acids. Once considered inconsequential to the formation of the protein product, there is now significant evidence to suggest that codon usage affects co-translational protein folding and the final structure of the expressed protein. Here we develop a method for computing and comparing codon-specific Ramachandran plots and demonstrate that the backbone dihedral angle distributions of some synonymous codons are distinguishable with statistical significance for some secondary structures. This shows that there exists a dependence between codon identity and backbone torsion of the translated amino acid. Although these findings cannot pinpoint the causal direction of this dependence, we discuss the vast biological implications should coding be shown to directly shape protein conformation and demonstrate the usefulness of this method as a tool for probing associations between codon usage and protein structure. Finally, we urge for the inclusion of exact genetic information into structural databases.

Optimism Is Prospectively Associated With Resilience During the COVID-19 Pandemic

Emerging research has identified how protective factors—like optimism—are associated with resilience to stress during the COVID-19 pandemic. However, the majority of research is cross-sectional, which creates ambiguity around the causal direction because these very protective factors might have also changed due to the pandemic. In the current study, we used longitudinal data from the Health and Retirement Study (N = 921; Mage = 64.54, SD = 10.71; 59.6% female; 57.5% White) to examine how optimism measured in 2016 predicted adjustment during the pandemic (in 2020). Higher baseline levels of optimism were subsequently associated with less worrying and stress resulting from changes in social contacts (βs > |.10|), less loneliness and not feeling overwhelmed (βs > |.16|), and greater COVID-related resilience and benefit-finding (β = .21). The findings will be discussed in the context of mechanisms that facilitate the protective functions of optimism and other psychological characteristics.

Causal Effects of Genetically Predicted Iron Status on Sepsis: A Two-Sample Bidirectional Mendelian Randomization Study

Background/Aim: Several observational studies showed a significant association between elevated iron status biomarkers levels and sepsis with the unclear direction of causality. A two-sample bidirectional mendelian randomization (MR) study was designed to identify the causal direction between seven iron status traits and sepsis.Methods: Seven iron status traits were studied, including serum iron, ferritin, transferrin saturation, transferrin, hemoglobin, erythrocyte count, and reticulocyte count. MR analysis was first performed to estimate the causal effect of iron status on the risk of sepsis and then performed in the opposite direction. The multiplicative random-effects and fixed-effects inverse-variance weighted, weighted median-based method and MR-Egger were applied. MR-Egger regression, MR pleiotropy residual sum and outlier (MR-PRESSO), and Cochran's Q statistic methods were used to assess heterogeneity and pleiotropy.Results: Genetically predicted high levels of serum iron (OR = 1.21, 95%CI = 1.13–1.29, p = 3.16 × 10−4), ferritin (OR = 1.32, 95%CI = 1.07–1.62, p =0.009) and transferrin saturation (OR = 1.14, 95%CI = 1.06–1.23, p = 5.43 × 10−4) were associated with an increased risk of sepsis. No significant causal relationships between sepsis and other four iron status biomarkers were observed.Conclusions: This present bidirectional MR analysis suggested the causal association of the high iron status with sepsis susceptibility, while the reverse causality hypothesis did not hold. The levels of transferrin, hemoglobin, erythrocytes, and reticulocytes were not significantly associated with sepsis. Further studies will be required to confirm the potential clinical value of such a prevention and treatment strategy.

THE EFFECT OF SAVING AND CAPITAL EXPANSION ON FINANCIAL AND TECHNOLOGICAL DEVELOPMENT IN GCC COUNTRIES

This study examined the effect of the relationship between saving and capital expansion on financial and technological development in three GCC countries using panel data from 1990 to 2019. The study used panel least squares, feasible general least squares, dynamic ordinary least squares and fully modified ordinary least squares used in the study. The findings showed that there was a significant positive long-run relationship between capital expansion and financial development and was a positive and insignificant long-run relationship between saving and financial development. Conversely, the study showed that there was a significant positive long-run relationship between saving and technological development. Meanwhile, there was a negative long-run relationship between capital expansion and technological development. Pairwise Granger causality test results showed that there was bidirectional causality between saving and financial development, a single causal direction from Adjusted net national income and financial development and a single causal direction from technological development and saving and Inflation, consumer prices. The main conclusions of the study were saving tends to support technological development, while investment tends to improve financial development. Therefore, GCC countries should formulate a long-term growth strategy in all sectors to determine their development requirements in light of the available resources.

Policy Preferences Influence Vote Choice When A New Party Emerges: Evidence from the 2017 French Presidential Election

A common explanation for electoral victories is that the winning candidate adopted issue positions that appealed to voters, implying that citizens’ choices are based on policy preferences. However, it is not straightforward to determine the causal direction between citizens’ issue preferences and their party choice. An alternative possibility, strongly supported by prior research, is that voters adopt the positions of the parties they vote for to rationalize their votes. The 2017 French presidential election offers a unique opportunity to address that question, as it saw the victory of a candidate who was not backed by one of the established parties. Using panel data, we show that policy preferences measured prior to Macron’s emergence as a candidate led voters with a particular bundle of preferences to support him. We conclude that policy preferences clearly do matter to vote choice and that this effect is most visible when a new party emerges.

Relationships Between Food Groups and Eating Time Slots According to Diabetes Status in Adults From the UK National Diet and Nutrition Survey (2008–2017)

Time of eating is associated with diabetes and obesity but little is known about less healthy foods and specific time of their intake over the 24 h of the day. In this study, we aimed to identify potential relationships between foods and their eating time and to see whether these associations may vary by diabetes status. The National Diet and Nutrition Survey (NDNS) including 6,802 adults (age ≥ 19 years old) collected 749,026 food recordings by a 4-day-diary. The contingency table cross-classifying 60 food groups with 7 pre-defined eating time slots (6–9 a.m., 9 a.m.–12 p.m., 12–2 p.m., 2–5 p.m., 8–10 p.m., 10 p.m.–6 a.m.) was analyzed by Correspondence Analysis (CA). CA biplots were generated for all adults and separately by diabetes status (self-reported, pre-diabetes, undiagnosed-diabetes, and non-diabetics) to visually explore the associations between food groups and time of eating across diabetes strata. For selected food groups, odds ratios (OR, 99% CI) were derived of consuming unhealthy foods at evening/night (8 p.m.–6 a.m.) vs. earlier time in the day, by logistic regression models with generalized estimating equations. The biplots suggested positive associations between evening/night and consumption of puddings, regular soft drinks, sugar confectioneries, chocolates, beers, ice cream, biscuits, and crisps for all adults in the UK. The OR (99% CIs) of consuming these foods at evening/night were, respectively, 1.43 (1.06, 1.94), 1.72 (1.44, 2.05), 1.84 (1.31, 2.59), 3.08 (2.62, 3.62), 7.26 (5.91, 8.92), 2.45 (1.84, 3.25), 1.90 (1.68, 2.16), and 1.49 (1.22, 1.82) vs. earlier time in the day adjusted for age, sex, body mass index (BMI), and social-economic levels. Stratified biplots found that sweetened beverages, sugar-confectioneries appeared more strongly associated with evening/night among undiagnosed diabetics. Foods consumed in the evening/night time tend to be highly processed, easily accessible, and rich in added sugar or saturated fat. Individuals with undiagnosed diabetes are more likely to consume unhealthy foods at night. Further longitudinal studies are required to ascertain the causal direction of the association between late-eating and diabetes status.

Biological Essentialism Correlates with (But Doesn’t Cause?) Intergroup Bias

People with biological essentialist beliefs about social groups also tend to endorse biased beliefs about individuals in those groups, including stereotypes, prejudices, and intensified emphasis on the group. These correlations could be due to biological essentialism causing bias, and some experimental studies support this causal direction. Given this prior work, we expected to find that biological essentialism would lead to increased bias compared to a control condition and set out to extend this prior work in a new direction (regarding “value-based” essentialism). But although the manipulation affected essentialist beliefs and essentialist beliefs were correlated with stereotyping (Studies 1, 2a, and 2b), prejudice (Studies 2a), and group emphasis (Study 3), there was no evidence that biological essentialism caused these outcomes. Given these findings, our initial research question became moot, and the present work focuses on reexamining the relationship between essentialism and bias. We discuss possible moderators, reverse causation, and third variables.

Investigating scientific mobility in co-authorship networks using multilayer temporal motifs

This paper introduces a framework for understanding complex temporal interaction patterns in large-scale scientific collaboration networks. In particular, we investigate how two key concepts in science studies, scientific collaboration and scientific mobility, are related and possibly differ between fields. We do so by analyzing multilayer temporal motifs: small recurring configurations of nodes and edges.Driven by the problem that many papers share the same publication year, we first provide a methodological contribution: an efficient counting algorithm for multilayer temporal motifs with concurrent edges. Next, we introduce a systematic categorization of the multilayer temporal motifs, such that each category reflects a pattern of behavior relevant to scientific collaboration and mobility. Here, a key question concerns the causal direction: does mobility lead to collaboration or vice versa? Applying this framework to scientific collaboration networks extracted from Web of Science (WoS) consisting of up to 7.7 million nodes (authors) and 94 million edges (collaborations), we find that international collaboration and international mobility reciprocally influence one another. Additionally, we find that Social sciences & Humanities (SSH) scholars co-author to a greater extent with authors at a distance, while Mathematics & Computer science (M&C) scholars tend to continue to collaborate within the established knowledge network and organization.