Causal Questions Research Articles

On the causal interpretation of heritability from a structural causal modeling perspective

Heritability estimated using the analysis of variance (ANOVA) for ascribing causal responsibility to genes for a phenotype has been criticized widely. First, there are problems associated with articulating the exact causal meaning of heritability in the standard model. Second, in conditions of gene–environment interaction or covariation that violate the assumptions made by the standard model, a causal interpretation of heritability is thought to be unwarranted. This paper aims to rethink these ideas and associated disputes from a structural causal modeling (SCM) perspective. Using SCM, we show that, in the standard model, heritability reflects the causal effect of eliminating genotypic differences on the change of phenotypic variance of a population. In the presence of interaction or covariation, heritability is estimated incorrectly using ANOVA. However, SCM can provide the causal effect of genotypes on the phenotypic variance regarding particular interventions. We also show that SCM can identify different types of causal effect and answer individual-level causal questions. We conclude that SCM has the resources to provide a systematic causal interpretation that can supplement traditional heritability estimates via ANOVA and offer a more substantial causal analysis of genetic causation.

Estimators for the value of the optimal dynamic treatment rule with application to criminal justice interventions.

Given an (optimal) dynamic treatment rule, it may be of interest to evaluate that rule - that is, to ask the causal question: what is the expected outcome had every subject received treatment according to that rule? In this paper, we study the performance of estimators that approximate the true value of: (1) an a priori known dynamic treatment rule (2) the true, unknown optimal dynamic treatment rule (ODTR); (3) an estimated ODTR, a so-called "data-adaptive parameter," whose true value depends on the sample. Using simulations of point-treatment data, we specifically investigate: (1) the impact of increasingly data-adaptive estimation of nuisance parameters and/or of the ODTR on performance; (2) the potential for improved efficiency and bias reduction through the use of semiparametric efficient estimators; and, (3) the importance of sample splitting based on the cross-validated targeted maximum likelihood estimator (CV-TMLE) for accurate inference. In the simulations considered, there was very little cost and many benefits to using CV-TMLE to estimate the value of the true and estimated ODTR; importantly, and in contrast to non cross-validated estimators, the performance of CV-TMLE was maintained even when highly data-adaptive algorithms were used to estimate both nuisance parameters and the ODTR. In addition, we apply these estimators for the value of the rule to the "Interventions" study, an ongoing randomized controlled trial, to identify whether assigning cognitive behavioral therapy (CBT) to criminal justice-involved adults with mental illness using an ODTR significantly reduces the probability of recidivism, compared to assigning CBT in a non-individualized way.

Antineutrophil Cytoplasmic Antibody (ANCA)-Associated Glomerulonephritis in a 15-Year-Old Patient After Receiving the Second Dose of the BBIBP-CorV (Sinopharm) COVID-19 Vaccine: A Case Report

Introduction: Antineutrophil cytoplasmic autoantibody (ANCA)-associated vasculitis (AAV) is a group of multisystemic autoimmune diseases that can involve organs such as kidneys and respiratory tracts. Since the initiation of COVID-19 vaccines, there have been few cases of antineutrophil cytoplasmic antibody (ANCA) glomerulonephritis development after receiving vaccine doses. Case Presentation: This report describes a 15-year-old man who has developed ANCA-associated glomerulonephritis (AAGN) after receiving the second dose of BBIBP-CorV (Sinopharm) COVID-19 vaccine. Conclusions: Development of AAGN after both natural SARS-CoV-2 infection and following other vaccinations raises the question of the possible causality between the two. Considering the widespread use of COVID-19 vaccines, it is highly important to investigate their possible side effects.

The alliance construct in psychotherapies: from evolution to revolution in theory and research

The alliance construct in psychotherapies: from evolution to revolution in theory and research

Do P2Y12 receptor inhibitors prescribed poststroke modify the risk of cognitive disorder or dementia? Protocol for a target trial using multiple national Swedish registries

IntroductionThe target of a class of antiplatelet medicines, P2Y12R inhibitors, exists both on platelets and on brain immune cells (microglia). This protocol aims to describe a causal (based on a...

Invited Commentary: Conducting and Emulating Trials to Study Effects of Social Interventions.

All else being equal, if we had 1 causal effect we wished to estimate, we would conduct a randomized trial with a protocol that mapped onto that causal question, or we would attempt to emulate that target trial with observational data. However, studying the social determinants of health often means there are not just 1 but several causal contrasts of simultaneous interest and importance, and each of these related but distinct causal questions may have varying degrees of feasibility in conducting trials. With this in mind, we discuss challenges and opportunities that arise when conducting and emulating such trials. We describe designing trials with the simultaneous goals of estimating the intention-to-treat effect, the per-protocol effect, effects of alternative protocols or joint interventions, effects within subgroups, and effects under interference, and we describe ways to make the most of all feasible randomized trials and emulated trials using observational data. Our comments are grounded in the study results of Courtin et al. (Am J Epidemiol. 2022;191(8):1444–1452).

Estimating Individual Treatment Effects using Non-Parametric Regression Models: a Review

Abstract Large observational data are increasingly available in disciplines such as health, economic and social sciences, where researchers are interested in causal questions rather than prediction. In this paper, we examine the problem of estimating heterogeneous treatment effects using non-parametric regression-based methods, starting from an empirical study aimed at investigating the effect of participation in school meal programs on health indicators. First, we introduce the setup and the issues related to conducting causal inference with observational or non-fully randomized data, and how these issues can be tackled with the help of statistical learning tools. Then, we review and develop a unifying taxonomy of the existing state-of-the-art frameworks that allow for individual treatment effects estimation via non-parametric regression models. After presenting a brief overview on the problem of model selection, we illustrate the performance of some of the methods on three different simulated studies. We conclude by demonstrating the use of some of the methods on an empirical analysis of the school meal program data.

When Coethnicity Fails

AbstractWhy do communities with larger shares of ethnic and racial minorities have worse public goods provision? Many studies have emphasized the role of diversity in hindering public outcomes, but the question of causality remains elusive. The authors contribute to this debate by tracing the roots of both contemporary racial demography and public goods provision to the uneven historical expansion of the state. Focusing on new historical data from Brazil, the authors show that more remote municipalities with lower levels of state capacity in the past were more frequently selected by escaped slaves to serve as permanent settlements. Consequently, such municipalities have worse public services and larger shares of Afro-descendants today. These results highlight the pervasive endogeneity of the relationship between ethnic demography and public outcomes. The failure to account for context-dependent historical confounders raises concerns about the validity of previous findings regarding the social costs and benefits of any particular demographic composition.

Mendelian randomization.

Mendelian randomization (MR) is a term that applies to the use of genetic variation to address causal questions about how modifiable exposures influence different outcomes. The principles of MR are based on Mendel's laws of inheritance and instrumental variable estimation methods, which enable the inference of causal effects in the presence of unobserved confounding. In this Primer, we outline the principles of MR, the instrumental variable conditions underlying MR estimation and some of the methods used for estimation. We go on to discuss how the assumptions underlying an MR study can be assessed and give methods of estimation that are robust to certain violations of these assumptions. We give examples of a range of studies in which MR has been applied, the limitations of current methods of analysis and the outlook for MR in the future. The difference between the assumptions required for MR analysis and other forms of non-interventional epidemiological studies means that MR can be used as part of a triangulation across multiple sources of evidence for causal inference.

Commentary on Whitsel et al.: Smoking, alcohol use and the brain- the challenge of answering causal questions.

Commentary on Whitsel et al.: Smoking, alcohol use and the brain- the challenge of answering causal questions.

Oboljenje kovid 19 - povreda na radu ili profesionalna bolest?

The COVID-19 disease pandemic has opened a number of legal issues, one of which is the need to define COVID-19 as a possible consequence of performing work. In that sense, the question arose whether it could be qualified as an injury at work or as an occupational disease. When it comes to the comparative law, this need to put COVID-19 in a professional context has already been answered in various ways, and the wandering in that process (due to the complexity of the issue) is probably best illustrated by the fact that in some systems it takes on the nature of a legal chameleon that is adjusting itself to a sector of work or to a period of exposure to the virus. In the Republic of Serbia, on the other hand, the controversy on this issue is still ongoing - which is also the reason why a review of comparative legal experiences is necessary. And while the qualification of COVID-19 as an injury at work is problematic due to the fact that it is hard to define an event that could be characterized as an accident at work that produced such a consequence, the qualification of COVID-19 as an occupational disease encounters other problems. Most of those problems in the Republic of Serbia, on the other hand, are systemic in nature and, therefore, require fixes of the existing legal gaps and systematic changes of the existing regulations by the Serbian lawmaker. Additionally, when it comes to COVID-19, the one question that arises is the question of causality - having in mind that in this day and age we are all in contact with the SARS-CoV-2 virus to a greater or to a lesser extent. In that sense, occupational medicine will be entrusted with the difficult task of determining the occupational origin of this disease - since it does not seem fair to recognize the status of an occupational disease to an employee if there are reasonable suspicions on the matter whether the infection took place during his performance of his work tasks.

Community Newsletter: Whole-genome sequencing from Korea, a question of causality, early milestones

Community Newsletter: Whole-genome sequencing from Korea, a question of causality, early milestones

Why Africa? Towards a materialist understanding of racism and the African slave trade

Common historical interpretations of the Atlantic slave system often lose the position Africa within the political-economic haze of the era, either producing confused accounts or eliding the question of causality altogether. I argue that this tendency stems from the corrosive effects of the historian’s position as an observer from the present, and that to understand why Africa was the prime location for the source of human slaves, one needs to take a materialist approach to the problem of origins. Only through careful examination of empire, the plantation complex, and the genesis of the Atlantic working classes can one arrive at a proper understanding of the thorny but interrelated issues of race and the beginnings of the African slave trade.

Discovering What Mattered: Answering Reverse Causal Questions by Detecting Unknown Treatment Assignment and Timing as Breaks in Panel Models

Discovering What Mattered: Answering Reverse Causal Questions by Detecting Unknown Treatment Assignment and Timing as Breaks in Panel Models

Long-Term Respiratory Consequences of Early-Life Respiratory Viral Infections: A Pragmatic Approach to Fundamental Questions

Early-life viral infection can have profound effects on the developing lung and immune systems, both important in asthma development. For decades, research has aimed to establish whether there is a causal link between these viral infections as an exposure and asthma later in childhood. Establishing causality will remain important, but new insights regarding early-life viral infection as an exposure, the recognition of asthma as a heterogeneous outcome, and the shared genetic susceptibility to both suggest a refocus from answering the theoretical question of causality toward additional pragmatic approaches focusing on improving patient outcomes across the spectrum of respiratory disease. This Clinical Commentary reviews the evidence on the consequences of early-life viral infection and aims to look beyond the question of causality, suggesting a research agenda specifically aimed at what matters for human development, and for the quality of life of current and future patients with wheezing disorders.

Machine learning for financial forecasting, planning and analysis: recent developments and pitfalls

This article is an introduction to machine learning for financial forecasting, planning and analysis (FP&A). Machine learning appears well suited to support FP&A with the highly automated extraction of information from large amounts of data. However, because most traditional machine learning techniques focus on forecasting (prediction), we discuss the particular care that must be taken to avoid the pitfalls of using them for planning and resource allocation (causal inference). While the naive application of machine learning usually fails in this context, the recently developed double machine learning framework can address causal questions of interest. We review the current literature on machine learning in FP&A and illustrate in a simulation study how machine learning can be used for both forecasting and planning. We also investigate how forecasting and planning improve as the number of data points increases.

“How will you construct a pathway system?”: Microanalysis of teacher-child scientific conversations

During the preschool years, children’s question-explanation exchanges with teachers serve as a powerful mechanism for their early STEM knowledge acquisition. Utilizing naturalistic longitudinal classroom data, we examined how such conversations in an inquiry-based preschool classroom change during an extended scientific inquiry unit. We were particularly interested in information-seeking questions (causal, e.g. “How will you construct a pathway?”; fact-based, e.g., “Where’s the marble?”). Videos (n = 18; 14 hours) were collected during a three-week inquiry unit on forces and motion and transcribed in CLAN-CHILDES software at the utterance level. Utterances were coded for delivery (question vs. statement) and content (e.g., fact-based, causal). Although teachers ask more questions than children, we found a significant increase in information-seeking questions during Weeks 2 and 3. We explored the content of information-seeking questions and found that the majority of these questions were asked by teachers, and focused on facts. However, the timing of fact-based and causal questions varied. Whereas more causal questions occurred in earlier weeks, more fact-based questions were asked towards the end of the inquiry. These findings provide insight into how children’s and teacher’s questions develop during an inquiry, informing our understanding of early science learning. Even in an inquiry-learning environment, teachers guide interactions, asking questions to support children’s learning. Children’s information-seeking questions increase during certain weeks, suggesting that providing opportunities to ask questions may allow children to be more active in constructing knowledge. Such findings are important for considering how science questions are naturally embedded in an inquiry-based learning classroom.

The relationship between equine pastern dermatitis, meteorological factors, and the skin microbiota.

BackgroundEquine pastern dermatitis (EPD) is a multifactorial syndrome, with prolonged exposure to moisture assumed to be a predisposing or primary factor.Hypothesis/ObjectivesTo examine the course of EPD lesion severity, changes in bacterial skin microbiota, and the influence of meteorological factors.AnimalsProspective, longitudinal cohort study over a one‐year period, with six Franches‐Montagnes stallions, four affected by EPD and two unaffected, that were kept under the same conditions.Methods and materialsPasterns were scored for lesion severity and sampled once a month for 12 consecutive months. Lesion severity, the skin microbiota and meteorological factors were examined for associations.ResultsEPD lesions tended to worsen in autumn and at the beginning of spring. The relationship between lesion severity and the meteorological factor precipitation was not clearly evident; high scores were preceded by both low or high rates of precipitation. Microbiota in affected pasterns appeared to have experienced a reduction in alpha diversity. Beta diversity analyses demonstrated that bacterial community structures were altered in affected versus unaffected pasterns, and that alterations were more pronounced with higher EPD scores (P = 0.005). Meteorological factors also had considerable influences on the bacterial composition, whereby these influences appeared to be more marked in the affected pasterns (P = 0.001, F = 3.19) than in unaffected ones (P = 0.005, F = 1.83).Conclusions and clinical relevanceOur study provides preliminary observations of the relationships between lesion severity, meteorological factors and cutaneous bacteria. The population was too small to draw firm conclusions, and further studies on environmental factors and the involvement of bacteria in this multifactorial disease are needed.

Happy, sad, or yucky? Parental emotion talk with infants in a book-sharing task.

While preschoolers consistently produce and use labels for happy and sad emotional states, labels for other emotional states (e.g., disgust) emerge much later in development. One explanation for these differences may lie in how parents first talk about these emotions with their children in infancy and toddlerhood. The current study examined parent talk about different emotions (i.e., happiness, sadness, anger, fear, and disgust) in a book-sharing task with their 12- to 24-month-old infants. Parental talk on each emotion page was coded for both quantity and quality of emotion talk. We found that, rather than labeling or asking questions about disgust emotional states, parents instead elaborated on and asked questions about the context of disgust pictures. In contrast, parents frequently labeled happy and sad emotional states and behaviors. Parental use of causal questions related to infants' productive emotion vocabularies. These different narrative styles may partly explain why older children acquire emotion labels for "happy" and "sad" much earlier than "disgust."

The Tourism–Poverty Nexus: Does Tourism Spending Influence Poverty Rates? Empirical Evidence from Regional Vietnamese Data

ABSTRACT This paper addresses the question of whether tourism spending will decrease poverty. Real incomes and tourism spending are usually positively correlated, thus complicating the question of causality from tourism spending to poverty. Regional Vietnamese data are applied in a test of whether tourism spending – in a model of poverty – will increase the forecasting performance compared to a model including only incomes. The test results reveal no improvement of the forecasting performance when tourism spending is included. Thus, there is no evidence of a direct link from tourism spending to poverty, but there may be indirect effects via incomes.