The Gap-Closing Estimand: A Causal Approach to Study Interventions That Close Disparities Across Social Categories

Disparities across race, gender, and class are important targets of descriptive research. But rather than only describe disparities, research would ideally inform interventions to close those gaps. The gap-closing estimand quantifies how much a gap (e.g., incomes by race) would close if we intervened to equalize a treatment (e.g., access to college). Drawing on causal decomposition analyses, this type of research question yields several benefits. First, gap-closing estimands place categories like race in a causal framework without making them play the role of the treatment (which is philosophically fraught for non-manipulable variables). Second, gap-closing estimands empower researchers to study disparities using new statistical and machine learning estimators designed for causal effects. Third, gap-closing estimands can directly inform policy: if we sampled from the population and actually changed treatment assignments, how much could we close gaps in outcomes? I provide open-source software (the R package gapclosing) to support these methods.

Improving local prevalence estimates of SARS-CoV-2 infections using a causal debiasing framework

Global and national surveillance of SARS-CoV-2 epidemiology is mostly based on targeted schemes focused on testing individuals with symptoms. These tested groups are often unrepresentative of the wider population and exhibit test positivity rates that are biased upwards compared with the true population prevalence. Such data are routinely used to infer infection prevalence and the effective reproduction number, Rt, which affects public health policy. Here, we describe a causal framework that provides debiased fine-scale spatiotemporal estimates by combining targeted test counts with data from a randomized surveillance study in the United Kingdom called REACT. Our probabilistic model includes a bias parameter that captures the increased probability of an infected individual being tested, relative to a non-infected individual, and transforms observed test counts to debiased estimates of the true underlying local prevalence and Rt. We validated our approach on held-out REACT data over a 7-month period. Furthermore, our local estimates of Rt are indicative of 1-week- and 2-week-ahead changes in SARS-CoV-2-positive case numbers. We also observed increases in estimated local prevalence and Rt that reflect the spread of the Alpha and Delta variants. Our results illustrate how randomized surveys can augment targeted testing to improve statistical accuracy in monitoring the spread of emerging and ongoing infectious disease.

From Speech Acoustics to Communicative Participation in Dysarthria: Toward a Causal Framework

Purpose: We proposed and tested a causal instantiation of the World Health Organization's International Classification of Functioning, Disability and Health (ICF) framework, linking acoustics, intelligibility, and communicative participation in the context of dysarthria. Method: Speech samples and communicative participation scores were collected from individuals with dysarthria ( n = 32). Speech was analyzed for two acoustic metrics (i.e., articulatory precision and speech rate), and an objective measure of intelligibility was generated from listener transcripts. Mediation analysis was used to evaluate pathways of effect between acoustics, intelligibility, and communicative participation. Results: We observed a strong relationship between articulatory precision and intelligibility and a moderate relationship between intelligibility and communicative participation. Collectively, data supported a significant relationship between articulatory precision and communicative participation, which was almost entirely mediated through intelligibility. These relationships were not significant when speech rate was specified as the acoustic variable of interest. Conclusion: The statistical corroboration of our causal instantiation of the ICF framework with articulatory acoustics affords important support toward the development of a comprehensive causal framework to understand and, ultimately, address restricted communicative participation in dysarthria.

Nexus Between CO2 Emissions, Energy Use and Economic Growth in Nepal

Background: Economic growth in different economies comes with a cost of environmental degradation. The environment-growth nexus has come to the spotlight since scientists as well as policy-makers point out the threat of climate change and global warming all around the world. Nepal faces problems of pollution day by day raising a question about sustainable growth in the country. Such sustainability can be achieved by exploiting the water resources of the country which can be further used to generate cleaner forms of energy. Objective: This paper examines the interconnection between environmental degradation and economic growth in Nepal under the Environmental Kuznets curve’s framework and causal framework. These frameworks also incorporate energy variables such as electricity production, electricity and oil consumption at a disaggregated level to understand the energy growth nexus in Nepal. Method: The Auto-Regressive Distributed Lag model followed by TY Non-Granger Causality tests and variance decompositions are incorporated in the study to examine the EKC hypothesis and the nexus between energy and growth is analyzed through a multivariate framework. Result: Our result does not show the presence of the EKC hypothesis in the case of Nepal. However, the causal framework indicated that a percentage increase in electricity generation would lead to a reduction in carbon dioxide by 0.7%. The variance decomposition results showed that the impact of CO2 on GDP would decrease with horizons getting longer. On the other hand, the impact of electricity generation on CO2 on was found to be 78% in the longer horizon. Conclusion: Nepal should harness its potential of generating hydroelectricity to reduce environmental pollution as well as increase economic growth. Substituting the cleaner form of energy such as hydroelectricity can help in reducing the consumption of fossils and fuels as well as help in mitigating the pollution level in Nepal. This will further allow Nepal to be self-reliant since it has huge potential for generating hydroelectricity.

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.

An instance-specific causal framework for learning intercellular communication networks that define microenvironments of individual tumors

Cells within a tumor microenvironment (TME) dynamically communicate and influence each other's cellular states through an intercellular communication network (ICN). In cancers, intercellular communications underlie immune evasion mechanisms of individual tumors. We developed an instance-specific causal analysis framework for discovering tumor-specific ICNs. Using head and neck squamous cell carcinoma (HNSCC) tumors as a testbed, we first mined single-cell RNA-sequencing data to discover gene expression modules (GEMs) that reflect the states of transcriptomic processes within tumor and stromal single cells. By deconvoluting bulk transcriptomes of HNSCC tumors profiled by The Cancer Genome Atlas (TCGA), we estimated the activation states of these transcriptomic processes in individual tumors. Finally, we applied instance-specific causal network learning to discover an ICN within each tumor. Our results show that cellular states of cells in TMEs are coordinated through ICNs that enable multi-way communications among epithelial, fibroblast, endothelial, and immune cells. Further analyses of individual ICNs revealed structural patterns that were shared across subsets of tumors, leading to the discovery of 4 different subtypes of networks that underlie disparate TMEs of HNSCC. Patients with distinct TMEs exhibited significantly different clinical outcomes. Our results show that the capability of estimating instance-specific ICNs reveals heterogeneity of ICNs and sheds light on the importance of intercellular communication in impacting disease development and progression.

The Health of the Water Planet: Challenges and Opportunities in the Mediterranean Area: An Overview

According to the United Nations (2020), since the 1980s, the global overall rate of water use has grown by 1% per year, and it is projected that, by 2050, humanity’s water footprint could exceed 30% of current levels. This situation is in stark contrast to the path toward the Sustainable Development Goals, especially Goal 6, “clean water and sanitation”, which also influences Goal 14, “life below water”, and Goal 15, “life on land”. This is because the availability of water directly affects the food security and production capacity of each Country, and therefore its management is a crucial issue worthy of particular attention. Problems related to water security are particularly evident in the Mediterranean area, which is already facing high environmental challenges. It is an area severely affected by global warming; thus, it is one of the most vulnerable environments to climate change globally. It follows that the improper management of water resources could further worsen an already alarming situation. This research aims to study the main water-related challenges that Mediterranean Countries face, highlighting the significant problems that weaken each Country. In this regard, the indicators relating to Goal 6 were considered, to define each Country’s current state. However, for a correct understanding, the main problems these Countries face were researched through a critical review of the literature (Scopus, Google Scholar, Web of Science). In this way, we were able to underline the effects of human activities on the hydrosphere and the repercussions on various ecosystems, following the drivers-pressures-state-impact-response causal framework. The results suggest that there is still a long way for Mediterranean Countries to progress toward Agenda 2030, as they face problems related to chemical (nitrate, microplastics, heavy metals, pesticides, etc.) and biological (E. coli and other microorganisms) pollution, as well as saline aquifers, absent or obsolete infrastructures, and transboundary basins. Hence, this study aims to provide valuable tools for a better evaluation of water management in Mediterranean Countries.

A Causal Framework for Cross-Cultural Generalizability

Behavioral researchers increasingly recognize the need for more diverse samples that capture the breadth of human experience. Current attempts to establish generalizability across populations focus on threats to validity, constraints on generalization and the accumulation of large cross-cultural datasets. But for continued progress, we also require a framework that lets us determine which inferences can be drawn and how to make informative cross-cultural comparisons. We describe a generative causal modeling framework and outline simple graphical criteria to derive analytic strategies and implied generalizations. Using both simulated and real data, we demonstrate how to project and compare estimates across populations. We conclude with a discussion of how a formal framework for generalizability can assist researchers in designing more informative cross-cultural studies and thus provides a more solid foundation for cumulative and generalizable behavioral research.

Toward a hybrid causal framework for autonomous vehicle safety analysis

Autonomous Vehicles (AVs), also known as self-driving cars, are a potentially transformative technology, but developing and demonstrating AV safety remains an open question. AVs offer some unique challenges that stretch the limits of traditional safety engineering practices. Most current safety standards and methodologies in the AV industry were not originally intended for application to autonomous vehicles, and they have significant limitations and shortcomings. In this article, we analyze the literature to first build an argument that a new safety framework is needed for AVs. We then use the identified limitations of current methodologies as a basis to formulate a set of fundamental requirements that must be met by any proposed AV safety framework. We propose a new AV safety framework based on the Hybrid Causal Logic (HCL) methodology, which combines Event Sequence Diagrams (ESDs), Fault Tree Analysis (FTA), and Bayesian Networks (BNs). The HCL framework is developed at a conceptual level and then evaluated versus the identified fundamental requirements. To further illustrate how the framework may meet the requirements, a simple example of an AV perception system scenario is developed using the HCL framework and evaluated. The results demonstrate that the HCL framework provides an integrated approach that has the potential to satisfy more completely the fundamental requirements than the current methodologies.