Evidence for interactive common causes. Resuming the Cartwright-Hausman-Woodward debate

The most serious candidates for common causes that fail to screen off (‘interactive common causes’, ICCs) and thus violate the causal Markov condition (CMC) refer to quantum phenomena. In her seminal debate with Hausman and Woodward, Cartwright early on focussed on unfortunate non-quantum examples. Especially, Hausman and Woodward’s redescriptions of quantum cases saving the CMC remain unchallenged. This paper takes up this lose end of the discussion and aims to resolve the debate in favour of Cartwright’s position. It systematically considers redescriptions of ICC structures, including those by Hausman and Woodward, and explains why these are inappropriate, when quantum mechanics (in an objective collapse interpretation) is true. It first shows that all cases of purported quantum ICCs are cases of entanglement and then, using the tools of causal modelling, it provides an analysis of the quantum mechanical formalism for the case that the collapse of entangled systems is best described as a causal model with an ICC.

The state of capacity development evaluation in biodiversity conservation and natural resource management

Capacity development is critical to long-term conservation success, yet we lack a robust and rigorous understanding of how well its effects are being evaluated. A comprehensive summary of who is monitoring and evaluating capacity development interventions, what is being evaluated and how, would help in the development of evidence-based guidance to inform design and implementation decisions for future capacity development interventions and evaluations of their effectiveness. We built an evidence map by reviewing peer-reviewed and grey literature published since 2000, to identify case studies evaluating capacity development interventions in biodiversity conservation and natural resource management. We used inductive and deductive approaches to develop a coding strategy for studies that met our criteria, extracting data on the type of capacity development intervention, evaluation methods, data and analysis types, categories of outputs and outcomes assessed, and whether the study had a clear causal model and/or used a systems approach. We found that almost all studies assessed multiple outcome types: most frequent was change in knowledge, followed by behaviour, then attitude. Few studies evaluated conservation outcomes. Less than half included an explicit causal model linking interventions to expected outcomes. Half of the studies considered external factors that could influence the efficacy of the capacity development intervention, and few used an explicit systems approach. We used framework synthesis to situate our evidence map within the broader literature on capacity development evaluation. Our evidence map (including a visual heat map) highlights areas of low and high representation in investment in research on the evaluation of capacity development.

The causal model of health literacy and health behavior for obesity prevention among primary school students in Bangkok, Thailand

Background: The aim of the study was to develop a research instrument to study the levels of health literacy for obesity prevention (HLFOP), as well as health behavior for obesity prevention (HBFOP). In addition, we investigated the causal model between health literacy and health behavior for obesity prevention among primary school students in Bangkok, Thailand. Methods: A cross-sectional study among 600 participants who were primary school students (aged 9-13 years) was conducted. The participants were selected from schools in all parts of Bangkok using multi-stage random sampling technique. The research instrument to assess HLFOP and HBFOP, constructed by the researchers, were utilized for data collection. Data were analyzed using descriptive statistics, exploratory and confirmatory factor analyses, and structural equation model through linear structural relationship. Results: We found that HBFOP was directly influenced by heath literacy in the category of Critical Literacy with an effect size of 0.65 (p < 0.01), and was indirectly influenced in the category of Basic Literacy and Interactive Literacy through Critical Literacy with effect sizes of 0.46 and 0.58 (p<0.01), respectively. The model was consistent with the empirical data, with Chi-Square=13.68, df=7, p=0.05721, RMSEA (root mean square error of approximation)= 0.040, SRMR (standardized root mean square residual)= 0.017 NFI (normal fit index)=0.99, GFI (goodness of fit index)=0.99, and AGFI (adjusted goodness of fit index)=0.97. Conclusions: HLFOP was influential on HBFOP in primary school students in the Bangkok Metropolis. The categories that were particularly influential were: 1) Basic Literacy: accessing health information skills; 2) Interactive Literacy: communication skills; and 3) Critical Literacy: media literacy and self-management skills.

Organizational citizenship behaviors perceived by collectivistic 50-and-older customers and medical-care service performance: an application of stimulus-organism-response theory

PurposeThe study aims to examine the underexplored agenda in organizational citizenship behaviors (OCBs) through the collectivistic 50-and-older customers' lens when encountering medical-care services by applying stimulus-organism-response (S-O-R) theory.Design/methodology/approachThe authors propose an integrative causal model derived from employees OCBs perceived by the collectivistic 50-and-older outpatients in Korean medical-care organizations and test the causal relationships using structural equation modeling (SEM).FindingsThe three dimensions of OCBs are external stimuli to the synergistic relationship of both cognitive and affective organisms for enhancing the organization's external outcomes. The customers' organismic processes mediate the relationships between OCBs and the resultant outcomes. Customer satisfaction plays a pivotal role in determining customers' future behavior when converting the business relationship to friendship.Practical implicationsThe proposed integrated model provides an overall mechanism of the collectivistic customer decision process in the medical-care service setting. The integrated model helps to understand better how customers proceed mental and emotional states with the encountered services and how frontline employees offer extra-roles beyond in-roles to their customers in touching points to maintain superior organizational performance.Originality/valueThe authors respond to the underexplored agenda in the OCB research discipline. The study is one of the few studies to examine the effect of OCBs from collectivistic customers' perspectives and apply a consumer behavior theory to explain a service organizational performance in an integrative causal model.

Efficiency of the primary health care policy in Brazilian municipalities: an application of the Pearl’s Structural Causal Model

ABSTRACT Background In recent years, public health policies and their effects on improving health outcomes have been gaining prominence in the economic literature and on the agenda of international organizations. Objective This study aims to evaluate the causal effect of the “Pacto pela Saúde” (Pact for Health) program on health policy performance in terms of a Health Vulnerability Index (HVI) of Brazilian municipalities from 2006 to 2013. The “Pacto pela Saúde” program is the current operational standard of the Brazilian Unified Health System (SUS). One of the main guidelines of this program was to improve health policy governance. Method The effect resulting from efficiency gains of the participation of municipalities in the health policy on the HVI was estimated by the Pearl’s Structural Causal Model. Results The results indicate a positive and significant impact of efficiency management on the reduction of health vulnerability in the municipalities. The Pearl’s Causal Model and the back-door criterion of causal identification were employed to calculate the effects of the “Pacto pela Saúde” program on the HVI. Conclusion The use of Pearl’s method in this study contributed to a more comprehensive analysis of the effects of the “Pacto pela Saúde” program on health outcomes and, therefore, its use in future research on the analysis of public policies is recommended.

Reducing the Complexity of Casual Representation in Bayesian Belief Network

Background: Bayesian Belief Network (BBN) is a well-established causal framework that is widely adopted in various domains and has a proven track record of success in research and application areas. However, BBN has weaknesses in causal knowledge elicitation and representation. The representation of the joint probability distribution in the Conditional Probability Table (CPT) has increased the complexity and difficulty for the user either in comprehending the causal knowledge or using it as a front-end modelling tool. Methods: This study aims to propose a simplified version of the BBN ─ Bayesian causal model, which can represent the BBN intuitively and proposes an inference method based on the simplified version of BBN. The CPT in the BBN is replaced with the causal weight in the range of[-1,+1] to indicate the causal influence between the nodes. In addition, an inferential algorithm is proposed to compute and propagate the influence in the causal model. Results: A case study is used to validate the proposed inferential algorithm. The results show that a Bayesian causal model is able to predict and diagnose the increment and decrement as in BBN. Conclusions: The Bayesian causal model that serves as a simplified version of BBN has shown its advantages in modelling and representation, especially from the knowledge engineering perspective.

Interventional Fairness with Indirect Knowledge of Unobserved Protected Attributes

The deployment of machine learning (ML) systems in applications with societal impact has motivated the study of fairness for marginalized groups. Often, the protected attribute is absent from the training dataset for legal reasons. However, datasets still contain proxy attributes that capture protected information and can inject unfairness in the ML model. Some deployed systems allow auditors, decision makers, or affected users to report issues or seek recourse by flagging individual samples. In this work, we examined such systems and considered a feedback-based framework where the protected attribute is unavailable and the flagged samples are indirect knowledge. The reported samples were used as guidance to identify the proxy attributes that are causally dependent on the (unknown) protected attribute. We worked under the causal interventional fairness paradigm. Without requiring the underlying structural causal model a priori, we propose an approach that performs conditional independence tests on observed data to identify such proxy attributes. We theoretically proved the optimality of our algorithm, bound its complexity, and complemented it with an empirical evaluation demonstrating its efficacy on various real-world and synthetic datasets.