Causal inference is a central goal of research. However, most psychologists refrain from explicitly addressing causal research questions and avoid drawing causal inference on the basis of nonexperimental evidence. We argue that this taboo against causal inference in nonexperimental psychology impairs study design and data analysis, holds back cumulative research, leads to a disconnect between original findings and how they are interpreted in subsequent work, and limits the relevance of nonexperimental psychology for policymaking. At the same time, the taboo does not prevent researchers from interpreting findings as causal effects-the inference is simply made implicitly, and assumptions remain unarticulated. Thus, we recommend that nonexperimental psychologists begin to talk openly about causal assumptions and causal effects. Only then can researchers take advantage of recent methodological advances in causal reasoning and analysis and develop a solid understanding of the underlying causal mechanisms that can inform future research, theory, and policymakers.
Growing evidence suggests inflammatory bowel disease (IBD) is linked to ischemic stroke (IS); however, the results are inconclusive. Therefore, it remains uncertain whether the association between IBD and IS is causal. Herein, we performed a bidirectional Mendelian randomization (MR) study to examine the causal association of IBD with IS.
Deer are the most abundant large herbivores in temperate and boreal forests across the Northern Hemisphere. They are ecosystem engineers known to alter understory vegetation and future tree species composition by selective browsing. Also, deer have strong impacts on faunistic groups, often mediated by vegetation.
Introduction: Cardiometabolic diseases, a major global health concern, stem from complex interactions of lifestyle, genetics, and biochemical markers. While extensive research has revealed strong associations between various risk factors and these diseases, latent confounding and limited causal discovery methods hinder understanding of their causal relationships, essential for mechanistic insights and developing effective prevention and intervention strategies.
Methods: We introduce anchorFCI, a novel adaptation of the conservative Really Fast Causal Inference (RFCI) algorithm, designed to enhance robustness and discovery power in causal learning by strategically selecting and integrating reliable anchor variables from a set of variables known not to be caused by the variables of interest.
Background: Observational studies have shown that hypothyroidism is strongly associated with adverse pregnancy outcomes, and that thyroxine during pregnancy comes mainly from the mother; therefore, thyroid defects in women may lead to problems such as miscarriage due to hormonal instability in early pregnancy, and foetal neurological deficits in mid- to late gestation, but whether there is a genetic causality between the two is still a matter of some controversy.
Objective: Goal to investigate the possible causal association between hypothyroidism and unfavorable pregnancy outcomes through the use of bioinformatics and Mendelian randomization (MR).
Methods: We used Mendelian randomization (MR) analyses using single nucleotide polymorphism (SNP) sites as instrumental variables to infer causal associations between exposures and outcomes.
Motivation: Gene expression prediction plays a vital role in transcriptome-wide association studies. Traditional models rely on genetic variants in close genomic proximity to the gene of interest to predict the genetic component of gene expression. Here, we propose a novel approach incorporating distal genetic variants acting through gene regulatory networks, in line with the omnigenic model of complex traits.
