Causal Assumptions Research Articles

Factors Affecting the Likelihood of Dogs and Cats Returning to Their Owners at a Municipal Animal Shelter in the United States

Introduction: Animal shelters consider return-to-owner (RTO) as an ideal outcome for animals, owners, and shelters. Methods to increase RTO likelihood are frequently discussed by shelter professionals nationwide. Some of these methods are evidence-based, while others are anecdotally successful. This retrospective study aimed to provide evidence for commonly suggested methods, as well as identify additional factors influencing RTO likelihood. Methods: Data from 5,960 dog and 3,489 cat impounds were obtained from a large municipal animal shelter in Utah, USA. Directed acyclic graphs were developed to visualize causal assumptions, which were used to identify confounders for adjustment in the logistic regression while modeling the associations between study variables and RTO outcomes for both dogs and cats. Results: Dogs and cats with microchips, older animals, healthy animals, neutered animals, and animals brought to the shelter via another public agency were more likely to return to their owners. Animal sex and season of impound did not affect either dogs’ or cats’ RTO likelihood. Conclusion: The characteristics influencing RTO likelihood were similar for both dogs and cats. These influences provide support for existing shelter practices, such as facilitating widespread microchipping and waiving reclamation fees, while also encouraging implementation of new practices, such as modifying stray hold periods based on source type or health status. Limitations of the study included the presence of incomplete information in the database and concerns with the generalizability of results to other shelters.

Patient satisfaction mediates the influence of trust, service quality and hospital sharia compliance on patient loyalty in Sharia hospitals in Riau province from an Islamic perspective

This study seeks to assess patient satisfaction by examining the impact of trust, service quality, and hospital Sharia compliance on patient loyalty at Sharia hospitals in Pekanbaru City, Riau Province. The study takes an Islamic perspective and aims to enhance competitiveness by focusing on the development of high-quality and dependable services. The research uses structural equation modeling, a statistical methodology that combines quantitative data and qualitative causal assumptions to assess and estimate causal links. The study employed a questionnaire to gather primary data from 342 respondents, including patients. The research findings are as follows: (1) Trust has a positive and significant impact on patient loyalty. (2) Service quality does not have a significant impact on patient loyalty. (3) Sharia compliance has a positive and significant impact on patient loyalty. (4) Trust has a positive and significant impact on patient satisfaction. (5) Service quality has a positive and significant impact on satisfaction. (6) Sharia compliance has a positive and significant impact on satisfaction. (7) Satisfaction positively and significantly mediates the effect of trust on loyalty. (8) Satisfaction positively and significantly mediates the influence of service quality on patient loyalty. (9) Satisfaction positively and significantly mediates the influence of Sharia compliance on loyalty. (10) Satisfaction has a positive and significant influence on loyalty.

Using causal loop diagrams to develop evaluative research propositions: opportunities and challenges in applications to nature‐based solutions

AbstractCausal loop diagrams (CLDs) are often used to provide an overview of important systemic elements related to an issue, rather than to inform empirical evaluations (studies which assess changes following an intervention using observed data). We suggest that empirical evaluations may benefit from the development of systems‐informed research propositions (specific testable causal assumptions with an emphasis on feedback loops) used to guide subsequent data collection, hypothesis testing and interpretation. We describe a qualitative systems‐thinking informed approach building on preexisting CLDs, published evidence, and expert/stakeholder consultation and reflect on our experience applying this to the early stages of two nature‐based solution (NBS) evaluations. We reflect on our experience and suggest that CLDs can be usefully employed to develop systems‐informed research propositions to inform subsequent empirical evaluation. This may lead to novel policy‐relevant research propositions which differ substantially from effectiveness‐oriented (“did it work?”) research questions. © 2023 The Authors. System Dynamics Review published by John Wiley & Sons Ltd on behalf of System Dynamics Society.

The effect of psychological factors on pain outcomes: lessons learned for the next generation of research.

Big data and machine learning techniques offer opportunities to investigate the effects of psychological factors on pain outcomes. Nevertheless, these advances can only deliver when the quality of the data is high and the underpinning causal assumptions are considered. We argue that there is room for improvement and identify some challenges in the evidence base concerning the effect of psychological factors on the development and maintenance of chronic pain. As a starting point, 3 basic tenets of causality are taken: (1) cause and effect differ from each other, (2) the cause precedes the effect within reasonable time, and (3) alternative explanations are ruled out. Building on these tenets, potential problems and some lessons learned are provided that the next generation of research should take into account. In particular, there is a need to be more explicit and transparent about causal assumptions in research. This will lead to better research designs, more appropriate statistical analyses, and constructive discussions and productive tensions that improve our science.

P82: A video-based qualitative analysis of case conferences to reduce BPSD

Background and objectives:Case conferences are described as a goal-oriented, systematic method that team members can use to develop person centred treatment actions for a particular care problem. However, not all case conferences have proven to be effective. The Norwegian Targeted Interdisciplinary Model for the Evaluation and Treatment of Neuropsychiatric Symptoms (TIME) is an effective multicomponent model based on case conferences that informs approaches to behavioural and psychological symptoms in residents with dementia. Our aim was to explore how TIME case conferences structured based on cognitive behavioural therapy (CBT) contributed to person-centred actions and how the specific structure of the TIME may have contributed to the effectiveness of the model.Methods:We used video observation of six case conferences. Videos in research provide extended opportunities for studying in detail the complexity of interactions that take place in social groups. We analysed these videos by iteratively watching them and performing a thematic cross-case analysis of their transcripts. Based on Habermas’s theory of communicative action, we emphasized what was talked about in the case conferences, and the display of communication between the participants in the case conferences.Results:Our findings showed that the theoretical principles behind the TIME, including both person- centred care and the inductive structure of CBT, reflected many aspects of Habermas’s theory of communicative actions. In particular, the TIME case conferences emphasized the lifeworld perspective for both residents, staff, and contributed to what Habermas labelled communicative rationality as a means to develop shared understanding among staff and create person-centred action.Conclusions:One causal assumption of how and why the TIME case conferences contributed to the effectiveness of the TIME in reducing BPSD in nursing homes is that the specific inductive structure of the case conferences with the column technique based on the ABC method together with PCC emphasized the importance of the lifeworld for both the resident and the staff. Even though case conferences have been highlighted as useful, it is not indifferent how these case conferences are structured and conducted.

The untapped potential of causal inference in cross-modal research

Cross-modal effects have recently become a popular topic in building science. However, studies in this area frequently neglect causal inference, leading to a lack of valid causal results. To address this problem, we specifically highlight causality and its importance to cross-modal research. We present three general guidelines, and describe them using toy examples, for appropriately conducting causal cross-modal research. The guidelines originate from the methodological framework for quantitative social science by Lundberg et al. (2021). They are as follows: i) specify the theoretical estimand as the target of causal inference; ii) specify the empirical estimand that is informative for the theoretical estimand based on causal assumptions; iii) select the estimation strategy empirically to estimate the empirical estimand. In light of these guidelines, we discuss some common methodological pitfalls in current research practices that can jeopardize causal inference. Moreover, we offer certain recommendations to avoid such pitfalls. The general objective of this paper is to promote transparent causal cross-modal research by raising the awareness of causal inference in view of appropriate causality-related methodological choices.

Association between psychological factors and molar-incisor hypomineralization: A cross-sectional study.

Molar-incisor hypomineralization (MIH) is a developmental enamel defect characterized by opacities from white to brownish color. A suspected multifactorial etiology has been suggested, whereas psychological factors during pregnancy have only been limitedly analyzed. We assessed the association between stress, depression, and anxiety in pregnancy and the presence of MIH in children at a later age. Using a cross-sectional Web-based questionnaire, we included 384 mothers who had children aged 6 and 12 years from Pasto, Colombia. Data were collected between October 2021 and March 2022. Sociodemographic variables; maternal and child factors related to prenatal, natal, or postnatal problems; and psychological factors such as stress and symptoms of anxiety and depression in pregnancy were inquired. Utilizing photographs depicting MIH lesions, mothers assessed their child's MIH status. A directed acyclic graph (DAG) analysis was performed to create causal assumptions, and logistic regression models were estimated to evaluate these assumptions. p-value was set at p < .05. The prevalence of MIH was 33.3%; 12.8% of the participants exhibited hypomineralization in both molars and incisors. DAG analysis and logistic regression models determined that MIH (present or not) was associated with symptoms of maternal depression (ORadj = 3.26, 95% CI: 1.92-5.52, p < .001), and MIH (both molars and incisors) was associated with symptoms of maternal anxiety (ORadj = 3.49, 95% CI: 1.80-6.76, p < .001). Psychological factors, among others, were significantly associated with the presence of MIH.

The Unfulfilled Promise of Longitudinal Designs for Causal Inference

Longitudinal designs are frequently used in psychological research. An intuitive analytic approach is to adjust for previous measurements to bolster the validity of causal conclusions when estimating the effect of a focal predictor (i.e., treatment) on an outcome. This approach is routinely applied but rarely substantiated in practice. What are the implications of adjusting for previous measurements? Does it necessarily improve causal inferences? In this paper, we demonstrate that answers to these questions are far from straightforward. We explain how adjusting for previous measurements can reduce or induce bias in common longitudinal scenarios. We further demonstrate, in scenarios with less stringent causal assumptions, adjusting or not adjusting for previous measurements can induce bias one way or the other. Put differently, adjusting or not adjusting for a previous measurement can simultaneously strengthen and undermine causal inferences from longitudinal research, even in the simplest scenarios. We urge researchers to overcome the unwarranted complacency brought on by using longitudinal designs to test causality. Practical recommendations for strengthening causal conclusions in psychology research are provided.

The intersection of learning difficulties and behavior problems – a scoping review of intervention research

Learning difficulties (LDif) and behavioral problems (BP) frequently co-occur. Affected students typically require interventions targeting learning-, social-, as well as emotional and behavioral-domains. The goal of this scoping review is therefore to provide an overview of the research on interventions that target these critical areas for students with or at-risk of disabilities. In total, 48 relevant studies were identified and analyzed regarding addressed competencies, target groups, the setting of the interventions as well as underlying causal assumptions. The review identified a variety of interventions and approaches designed to address LDif and BP, which makes it impossible to draw conclusions on a single best approach. Co-occurring LDif and BP reflect a plurality of difficulties that are incompatible with a ‘one size fits all’ approach. The vast majority of studies assumed that the relationship between LDif and BP are reciprocal/unidirectional. Few studies focused common variables that potentially affect both LDif and BP. In addition, the majority of studies addressed child-level variables. Future research should be conducted to focus on variables at the environmental or institutional level that might impact both LDif and BP.

Introduction to Causal Inference for Psychologists: Testable and Non-Testable Causal and Statistical Assumptions

The main goal of basic research is to answer causal questions. Generally, only the statistical part of this process tends to proceed in a partially formal way and according to clearly defined rules. At the same time, the causal relations are often treated informally or implicitly in a way that is prone to difficult-to-detect errors. This introduction aims to show psychology researchers some of the great benefits of approaching causal issues using a formal theory of causal inference. In this part, I discuss the non-obvious status and role of causal and statistical assumptions in causal inference. After covering, in a simple setting, the general shape of inference from causal assumptions, statistical assumptions, and data to causal effects, I outline, from a contemporary perspective, the limits of applicability of the general linear model. Then, I introduce the formal part of Pearl’s theory that relies on graphs. Using these tools, I show how one can analyze and interpret the results of an experiment on short-term memory search, and I discuss the back-door and front-door adjustments. To present the mathematical part of the theory in an accessible way without overly simplifying it, I illustrate some issues by using simulations written in R.

Hedges, mottes, and baileys: Causally ambiguous statistical language can increase perceived study quality and policy relevance.

There is a norm in psychology to use causally ambiguous statistical language, rather than straightforward causal language, when describing methods and results of nonexperimental studies. However, causally ambiguous language may inhibit a critical examination of the study's causal assumptions and lead to a greater acceptance of policy recommendations that rely on causal interpretations of nonexperimental findings. In a preregistered experiment, 142 psychology faculty, postdocs, and doctoral students (54% female), ages 22-67 (M = 33.20, SD = 8.96), rated the design and analysis from hypothetical studies with causally ambiguous statistical language as of higher quality (by .34-.80 SD) and as similarly or more supportive (by .16-.27 SD) of policy recommendations than studies described in straightforward causal language. Thus, using statistical rather than causal language to describe nonexperimental findings did not decrease, and may have increased, perceived support for implicitly causal conclusions.

Flexible Machine Learning Estimation of Conditional Average Treatment Effects: A Blessing and a Curse.

Causal inference from observational data requires untestable identification assumptions. If these assumptions apply, machine learning methods can be used to study complex forms of causal effect heterogeneity. Recently, several machine learning methods were developed to estimate the conditional average treatment effect (ATE). If the features at hand cannot explain all heterogeneity, the individual treatment effects can seriously deviate from the conditional ATE. In this work, we demonstrate how the distributions of the individual treatment effect and the conditional ATE can differ when a causal random forest is applied. We extend the causal random forest to estimate the difference in conditional variance between treated and controls. If the distribution of the individual treatment effect equals that of the conditional ATE, this estimated difference in variance should be small. If they differ, an additional causal assumption is necessary to quantify the heterogeneity not captured by the distribution of the conditional ATE. The conditional variance of the individual treatment effect can be identified when the individual effect is independent of the outcome under no treatment given the measured features. Then, in the cases where the individual treatment effect and conditional ATE distributions differ, the extended causal random forest can appropriately estimate the variance of the individual treatment effect distribution, whereas the causal random forest fails to do so.

Inferring Causality Is Preference-Sensitive: We Need a Book of Who as Well as Why.

In multiple publications over 3 decades, most recently in The Book of Why, Judea Pearl has led what he regards as the 'causal revolution'. His central contention is that, prior to it, no discipline had produced a rigorous 'scientific' way of making the causal inferences from observational data necessary for policy and decision making. The concentration on the statistical processing of data, outputting frequencies or probabilities, had proceeded without adequately acknowledging that this statistical processing is operating, not only on a particular set of data, but on a set of causal assumptions about that data, often unarticulated and unanalysed. He argues that the arrival of the directed acyclic graph (DAG), a 'language of causation' has enabled this fundamental weakness to be remedied. We outline the DAG approach to the extent necessary to make the key point, captured in this paper's title regarding DAG's potential contribution to improved decision or policy making.

Genetic architectures of cerebral ventricles and their overlap with neuropsychiatric traits.

The cerebral ventricles are recognized as windows into brain development and disease, yet their genetic architectures, underlying neural mechanisms and utility in maintaining brain health remain elusive. Here we aggregated genetic and neuroimaging data from 61,974 participants (age range, 9 to 98 years) in five cohorts to elucidate the genetic basis of ventricular morphology and examined their overlap with neuropsychiatric traits. Genome-wide association analysis in a discovery sample of 31,880 individuals identified 62 unique loci and 785 candidate genes associated with ventricular morphology. We replicated over 80% of loci in a well-matched cohort of lateral ventricular volume. Gene set analysis revealed enrichment of ventricular-trait-associated genes in biological processes and disease pathogenesis during both early brain development and degeneration. We explored the age-dependent genetic associations in cohorts of different age groups to investigate the possible roles of ventricular-trait-associated loci in neurodevelopmental and neurodegenerative processes. We describe the genetic overlap between ventricular and neuropsychiatric traits through comprehensive integrative approaches under correlative and causal assumptions. We propose the volume of the inferior lateral ventricles as a heritable endophenotype to predict the risk of Alzheimer's disease, which might be a consequence of prodromal Alzheimer's disease. Our study provides an advance in understanding the genetics of the cerebral ventricles and demonstrates the potential utility of ventricular measurements in tracking brain disorders and maintaining brain health across the lifespan.

Long-term particulate matter exposure and the risk of neurological hospitalization: Evidence from causal inference of a large longitudinal cohort in South China

With limited evidence on the neurological impact of particulate matter (PM) exposure in China, particularly for PM1 which is smaller but more toxic, we conducted a large Chinese cohort study using causal inference approaches to comprehensively clarify such impact. A total of 36,271 participants in southern China were recruited in 2015 and followed up through 2020. We obtained the neurological hospitalizations records by linking the cohort data to the electronic reports from 418 medical institutions across the study area. By using high-resolution PM concentrations from satellite-based spatiotemporal models and the cohort data, we performed marginal structural Cox models under causal assumptions to assess the potential causal links between time-varying PM exposure and neurological hospitalizations. Our findings indicated that increasing PM1, PM2.5, and PM10 concentrations by 1 μg/m³ were associated with higher overall neurological hospitalization risks, with hazard ratios (HRs) of 1.10 (95% confidence interval (CI) 1.04–1.16), 1.09 (95% CI 1.04–1.14), and 1.03 (95% CI 1.00–1.06), respectively. PM1 appeared to have a stronger effect on neurological hospitalization, with a 1% and 7% higher impact compared to PM2.5 and PM10, respectively. Additionally, each 1-μg/m3 increase in the annual PM1 concentration was associated with an elevated risk of hospitalizations for ischemic stroke (HR: 1.15; 95% CI, 1.06–1.26), which tended to be larger than the estimates for PM2.5 (HR: 1.13, 95% CI, 1.04–1.23) and PM10 (HR: 1.05, 95% CI, 1.00–1.09). Furthermore, never-married or female individuals tended be at a greater risk compared with their counterparts. Our study provides important insights into the health impact of particles, particularly smaller particles, on neurological hospitalization risk and highlights the need for clean-air policies that specifically target these particles.

Testing Causal Effects of Empathy on Children's Prosociality in Politeness Dilemmas - An Intervention Study.

Empathy is commonly considered a driver of prosociality in child ontogeny, but causal assumptions regarding this effect mostly rely on correlational research designs. Here, 96 urban German children (5-8 years; 48 girls; predominantly White; from mid-to-high socioeconomic backgrounds) participated in an empathy intervention or a control condition before prosocial behaviors (polite lie-telling: rating the drawing as good; prosocial encouragement: utterances interpreted as cheering up the artist) were assessed in an art-rating task. Contrasting children's empathy at baseline with their empathy after the intervention indicated promoted empathy compared to the control group. Despite the intervention's effect on children's empathy, there were no simultaneous changes in prosocial behaviors. At the same time, children's empathy at baseline was associated with their prosocial encouragement. These results indicate conceptual associations between children's empathy and prosociality. However, they do not support strict causal claims regarding this association in middle childhood. Further applications of the novel short-time intervention to address causal effects of empathy on prosociality and other developmental outcomes are discussed.

Evaluating genomic polygenic risk scores for childhood acute lymphoblastic leukemia in Latinos

The utility of polygenic risk score (PRS) models has not been comprehensively evaluated for childhood acute lymphoblastic leukemia (ALL), the most common type of cancer in children. Previous PRS models for ALL were based on significant loci observed in genome-wide association studies (GWASs), even though genomic PRS models have been shown to improve prediction performance for a number of complex diseases. In the United States, Latino (LAT) children have the highest risk of ALL, but the transferability of PRS models to LAT children has not been studied. In this study, we constructed and evaluated genomic PRS models based on either non-Latino White (NLW) GWAS or a multi-ancestry GWAS. We found that the best PRS models performed similarly between held-out NLW and LAT samples (PseudoR2= 0.086 ± 0.023 in NLW vs. 0.060 ± 0.020 in LAT), and can be improved for LAT if we performed GWAS in LAT-only (PseudoR2= 0.116 ± 0.026) or multi-ancestry samples (PseudoR2= 0.131 ± 0.025). However, the best genomic models currently do not have better prediction accuracy than a conventional model using all known ALL-associated loci in the literature (PseudoR2= 0.166 ± 0.025), which includes loci from GWAS populations that we could not access to train genomic PRS models. Our results suggest that larger and more inclusive GWASs may be needed for genomic PRS to be useful for ALL. Moreover, the comparable performance between populations may suggest a more oligogenic architecture for ALL, where some large effect loci may be shared between populations. Future PRS models that move away from the infinite causal loci assumption may further improve PRS for ALL.

An introduction to directed acyclic graphs in trauma research.

Directed acyclic graphs (DAGs) are visual representations of the presumed causal structure of an empirical research data set. They are important tools for researchers but have been used rarely in the psychological trauma literature. The purpose of this article is to explain what DAGs are and why (and how) they are useful for trauma researchers. We first describe the utility of DAGs for making causal assumptions explicit, identifying causal effects, and preventing bias. Basic definitions and rules governing the use of DAGs are presented using a hypothetical DAG. We explain why conditioning on a variable, for example, by controlling for it in a multivariable model, can in some circumstances actually introduce bias and not prevent it. We also provide references for topics related to DAGs that are beyond the scope of this introductory article. DAGs are illustrated using the example of the effect of posttraumatic stress disorder (PTSD) on Parkinson's disease. We demonstrate that a multivariable model controlling for all covariates that are being considered introduces bias and would make it impossible to identify the causal effect of PTSD on Parkinson's disease. DAGs can help trauma researchers to understand when they can and when they cannot draw causal conclusions based on research data. This introduction to DAGs should help readers understand their use in the articles on marginal structural models, causal mediation analysis, and instrumental variable methods in this special section, Causal inference and agent-based modeling in trauma research. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Intervention to reduce sedentary behaviour and improve outcomes after stroke (Get Set Go): a study protocol for the process evaluation of a pilot cluster randomised controlled trial (RECREATE)

IntroductionStroke survivors spend long periods of time engaging in sedentary behaviour (SB) even when their functional recovery is good. In the RECREATE programme, an intervention aimed at reducing SB (‘Get...

Conditional average treatment effect estimation with marginally constrained models

Abstract Treatment effect estimates are often available from randomized controlled trials as a single average treatment effect for a certain patient population. Estimates of the conditional average treatment effect (CATE) are more useful for individualized treatment decision-making, but randomized trials are often too small to estimate the CATE. Examples in medical literature make use of the relative treatment effect (e.g. an odds ratio) reported by randomized trials to estimate the CATE using large observational datasets. One approach to estimating these CATE models is by using the relative treatment effect as an offset, while estimating the covariate-specific untreated risk. We observe that the odds ratios reported in randomized controlled trials are not the odds ratios that are needed in offset models because trials often report the marginal odds ratio. We introduce a constraint or a regularizer to better use marginal odds ratios from randomized controlled trials and find that under the standard observational causal inference assumptions, this approach provides a consistent estimate of the CATE. Next, we show that the offset approach is not valid for CATE estimation in the presence of unobserved confounding. We study if the offset assumption and the marginal constraint lead to better approximations of the CATE relative to the alternative of using the average treatment effect estimate from the randomized trial. We empirically show that when the underlying CATE has sufficient variation, the constraint and offset approaches lead to closer approximations to the CATE.