CDAF: A Causality Driven Analysis Framework for Disease Risk Factor Identification

In the interconnection environment, people combine basic services into composite services to provide more complex function for sophisticated applications. Accordingly, service fault localization in composite services becomes a critical issue for guaranteeing the normal running of composite services. This paper proposes a novel Causal Inference based Service Dependency Graph (CISDG) for statistical service fault localization. Our approach first utilizes the dependencies between basic services in the composite services by transforming the service dependency graph into a causal graph. Then it intuitively applies the causal inference to service fault localization on the composite services. Our work mainly focuses on developing CISDG and the causal inference on CISDG. To develop CISDG, we characterize the dependency and causal relationships between basic services and the target causal graph. To perform the causal inference on CISDG, we apply the well-known causal inference techniques such as the Back-Door Criterion and enhance the algorithm of the network diagnostic algorithm based Causal Inference (CIND) to improve the efficiency of the statistical service fault localization. The case study illustrates that our approach has advantages over its rivals in the service fault localization of composite services.

Microbiome Medicine: This Changes Everything

We revisit the classic semi‐parametric problem of inference on a low‐dimensional parameter θ0 in the presence of high‐dimensional nuisance parameters η0. We depart from the classical setting by allowing for η0 to be so high‐dimensional that the traditional assumptions (e.g. Donsker properties) that limit complexity of the parameter space for this object break down. To estimate η0, we consider the use of statistical or machine learning (ML) methods, which are particularly well suited to estimation in modern, very high‐dimensional cases. ML methods perform well by employing regularization to reduce variance and trading off regularization bias with overfitting in practice. However, both regularization bias and overfitting in estimating η0 cause a heavy bias in estimators of θ0 that are obtained by naively plugging ML estimators of η0 into estimating equations for θ0. This bias results in the naive estimator failing to be N−1/2 consistent, where N is the sample size. We show that the impact of regularization bias and overfitting on estimation of the parameter of interest θ0 can be removed by using two simple, yet critical, ingredients: (1) using Neyman‐orthogonal moments/scores that have reduced sensitivity with respect to nuisance parameters to estimate θ0; (2) making use of cross‐fitting, which provides an efficient form of data‐splitting. We call the resulting set of methods double or debiased ML (DML). We verify that DML delivers point estimators that concentrate in an N−1/2‐neighbourhood of the true parameter values and are approximately unbiased and normally distributed, which allows construction of valid confidence statements. The generic statistical theory of DML is elementary and simultaneously relies on only weak theoretical requirements, which will admit the use of a broad array of modern ML methods for estimating the nuisance parameters, such as random forests, lasso, ridge, deep neural nets, boosted trees, and various hybrids and ensembles of these methods. We illustrate the general theory by applying it to provide theoretical properties of the following: DML applied to learn the main regression parameter in a partially linear regression model; DML applied to learn the coefficient on an endogenous variable in a partially linear instrumental variables model; DML applied to learn the average treatment effect and the average treatment effect on the treated under unconfoundedness; DML applied to learn the local average treatment effect in an instrumental variables setting. In addition to these theoretical applications, we also illustrate the use of DML in three empirical examples.

School aversion is increasingly recognized in adolescents and has been linked to emotional distress, suicide risk, and academic failure. However, studies rely on correlational analysis, making it unclear which risk factors truly cause school aversion. To address this gap, we combined directed acyclic graph (DAG)-based modeling, DoWhy-based causal effect estimation, and counterfactual simulation to explore psychosocial pathways related to school aversion in clinically referred adolescents. Participants were clinically referred adolescents from mental health services who completed standardized psychosocial assessments. Variables were obtained through validated instruments and clinical intake records, covering demographics, adversity, personality, and symptoms. Causal inference was conducted through a combined framework of DAG learning, DoWhy estimation with backdoor propensity-score weighting and logistic-model-based counterfactual simulation. All analyses were performed using Python 3.11.8, with pgmpy, DoWhy, and scikit-learn as core libraries. A total of 423 adolescents were assessed, with 57.7% showing school aversion or refusal and 39.7% reporting suicidal thoughts or attempts. DAG modeling identified several school aversion-adjacent relationships, with family structure, bullying, and digital addiction retained as primary school-aversion-centered pathways in downstream analyses, whereas trauma remained a nearby contextual factor. DoWhy analysis showed that the retained associations were SchoolAversion → Bullying [average treatment effect (ATE) = 0.046, p = 0.001] and SchoolAversion → DigitalAdd (ATE = 0.036, p = 0.008), while family structure showed a significant negative effect on school aversion (ATE = -0.162, p = 0.031). Counterfactual simulation showed that removing bullying or digital addiction had relatively small effects on school aversion. In exploratory counterfactual simulations, SchoolAversion → Suicide showed a risk difference of 0.266 (bootstrap95% CI 0.182 to 0.346). Among clinically referred adolescents, school aversion appeared not only as an outcome linked to upstream psychosocial context, but also as a central factor associated with downstream bullying, digital addiction, and suicide risk in the present framework. This integrated causal framework may help refine risk screening and intervention prioritization in clinical populations, while further validation in non-clinical and longitudinal samples is needed.

In this paper, we provide efficient estimators and honest confidence bands for a variety of treatment effects including local average (LATE) and local quantile treatment effects (LQTE) in data-rich environments. We can handle very many control variables, endogenous receipt of treatment, heterogeneous treatment effects, and function-valued outcomes. Our framework covers the special case of exogenous receipt of treatment, either conditional on controls or unconditionally as in randomized control trials. In the latter case, our approach produces efficient estimators and honest bands for (functional) average treatment effects (ATE) and quantile treatment effects (QTE). To make informative inference possible, we assume that key reduced form predictive relationships are approximately sparse. This assumption allows the use of regularization and selection methods to estimate those relations, and we provide methods for post-regularization and post-selection inference that are uniformly valid (honest) across a wide-range of models. We show that a key ingredient enabling honest inference is the use of orthogonal or doubly robust moment conditions in estimating certain reduced form functional parameters. We illustrate the use of the proposed methods with an application to estimating the effect of 401(k) eligibility and participation on accumulated assets. The results on program evaluation are obtained as a consequence of more general results on honest inference in a general moment condition framework, which arises from structural equation models in econometrics. Here too the crucial ingredient is the use of orthogonal moment conditions, which can be constructed from the initial moment conditions. We provide results on honest inference for (function-valued) parameters within this general framework where any high-quality, modern machine learning methods can be used to learn the nonparametric/high-dimensional components of the model. These include a number of supporting auxilliary results that are of major independent interest: namely, we (1) prove uniform validity of a multiplier bootstrap, (2) offer a uniformly valid functional delta method, and (3) provide results for sparsity-based estimation of regression functions for function-valued outcomes.

BackgroundAtopic Dermatitis (AD) is a chronic inflammatory skin disease affecting a significant portion of the global population. While conventional treatments effectively control and manage symptoms, there is a growing need for more personalized and precise approaches for patients. Precision medicine has emerged as a promising therapeutic strategy that tailors treatment to the individual characteristics of each patient. Complementing this, the Treat-to-Target (T2T) strategy sets specific clinical goals and involves continuous monitoring and treatment adjustments based on real-time patient responses and biomarker data.ObjectivesThis review aims to explore the latest advancements in precision medicine and the T2T strategy for AD.MethodsA comprehensive literature review was conducted to identify the most recent studies and advancements in precision medicine and the T2T strategy for AD.ResultsRecent studies highlight the effectiveness of precision medicine in achieving sustained remission and improving the quality of life for patients. The T2T strategy was shown to be effective in preventing exacerbations and adapting treatments based on real-time patient responses and biomarker data.Study limitationsThe lack of a consensus on the clinical implementation of precision medicine and T2T, limited longitudinal studies validating long-term outcomes, challenges in stratifying patients due to biomarker heterogeneity, and barriers to integrating emerging diagnostic technologies into routine practice.ConclusionsPrecision medicine and the T2T strategy represent significant advancements in the management of AD. These approaches offer the potential for more personalized, effective, and adaptive treatment protocols, leading to improved patient outcomes. Continuous research and development in these areas are essential to fully realize their potential and integrate them into routine clinical practice.

Precision medicine and targeted health education are revolutionizing chronic disease prevention, particularly in vulnerable maternal and child populations. With chronic conditions such as gestational diabetes, hypertension, and congenital disorders increasingly affecting maternal and neonatal health, there is an urgent need for personalized interventions that go beyond conventional public health approaches. Precision medicine, which leverages genetic profiling, biomarker analysis, and individualized treatment plans, allows for tailored prevention strategies that address genetic predispositions and environmental risk factors in at-risk populations. This paper explores the integration of precision medicine with health education initiatives to create a comprehensive framework for chronic disease prevention in maternal and child health. It examines the role of genomics, digital health technologies, and artificial intelligence in identifying high-risk groups and enabling early interventions. Additionally, the study emphasizes the importance of culturally sensitive health education programs that empower women with the knowledge and resources needed to make informed health decisions. By evaluating case studies of successful precision medicine applications and community-based education programs, this paper highlights best practices for reducing the burden of chronic diseases in underserved maternal and child populations. The findings suggest that a synergistic approach—combining data-driven personalized care with accessible, equity-focused health literacy initiatives—can significantly improve health outcomes. Ultimately, advancing precision medicine and health education will play a pivotal role in achieving sustainable disease prevention and promoting maternal and child well-being.

Developing Theory Using Machine Learning Methods

Rapid advances in precision medicine promise dramatic reductions in morbidity and mortality for a growing array of conditions. To realize the benefits of precision medicine and minimize harm, it is necessary to address real-world challenges encountered in translating this research into practice. Foremost among these is how to choose and use precision medicine modalities in real-world practice by addressing issues related to caring for the sizable proportion of people living with multimorbidity. Precision medicine needs to be delivered in the broader context of precision care to account for factors that influence outcomes for specific therapeutics. Precision care integrates a person-centered approach with precision medicine to inform decision making and care planning by taking multimorbidity, functional status, values, goals, preferences, social and societal context into account. Designing dissemination and implementation of precision medicine around precision care would improve person-centered quality and outcomes of care, target interventions to those most likely to benefit thereby improving access to new therapeutics, minimize the risk of withdrawal from the market from unanticipated harms of therapy, and advance health equity by tailoring interventions and care to meet the needs of diverse individuals and populations. Precision medicine delivered in the context of precision care would foster respectful care aligned with preferences, values, and goals, engendering trust, and providing needed information to make informed decisions. Accelerating adoption requires attention to the full continuum of translational research: developing new approaches, demonstrating their usefulness, disseminating and implementing findings, while engaging patients throughout the process. This encompasses basic science, preclinical and clinical research and implementation into practice, ultimately improving health. This article examines challenges to the adoption of precision medicine in the context of multimorbidity. Although the potential of precision medicine is enormous, proactive efforts are needed to avoid unintended consequences and foster its equitable and effective adoption.

Rapid advances in precision medicine promise dramatic reductions in morbidity and mortality for a growing array of conditions. To realize the benefits of precision medicine and minimize harm, it is necessary to address real-world challenges encountered in translating this research into practice. Foremost among these is how to choose and use precision medicine modalities in real-world practice by addressing issues related to caring for the sizable proportion of people living with multimorbidity. Precision medicine needs to be delivered in the broader context of precision care to account for factors that influence outcomes for specific therapeutics. Precision care integrates a person-centered approach with precision medicine to inform decision making and care planning by taking multimorbidity, functional status, values, goals, preferences, social and societal context into account. Designing dissemination and implementation of precision medicine around precision care would improve person-centered quality and outcomes of care, target interventions to those most likely to benefit thereby improving access to new therapeutics, minimize the risk of withdrawal from the market from unanticipated harms of therapy, and advance health equity by tailoring interventions and care to meet the needs of diverse individuals and populations. Precision medicine delivered in the context of precision care would foster respectful care aligned with preferences, values, and goals, engendering trust, and providing needed information to make informed decisions. Accelerating adoption requires attention to the full continuum of translational research: developing new approaches, demonstrating their usefulness, disseminating and implementing findings, while engaging patients throughout the process. This encompasses basic science, preclinical and clinical research and implementation into practice, ultimately improving health. This article examines challenges to the adoption of precision medicine in the context of multimorbidity. Although the potential of precision medicine is enormous, proactive efforts are needed to avoid unintended consequences and foster its equitable and effective adoption.

Rapid advances in precision medicine promise dramatic reductions in morbidity and mortality for a growing array of conditions. To realize the benefits of precision medicine and minimize harm, it is necessary to address real-world challenges encountered in translating this research into practice. Foremost among these is how to choose and use precision medicine modalities in real-world practice by addressing issues related to caring for the sizable proportion of people living with multimorbidity. Precision medicine needs to be delivered in the broader context of precision care to account for factors that influence outcomes for specific therapeutics. Precision care integrates a person-centered approach with precision medicine to inform decision making and care planning by taking multimorbidity, functional status, values, goals, preferences, social and societal context into account. Designing dissemination and implementation of precision medicine around precision care would improve person-centered quality and outcomes of care, target interventions to those most likely to benefit thereby improving access to new therapeutics, minimize the risk of withdrawal from the market from unanticipated harms of therapy, and advance health equity by tailoring interventions and care to meet the needs of diverse individuals and populations. Precision medicine delivered in the context of precision care would foster respectful care aligned with preferences, values, and goals, engendering trust, and providing needed information to make informed decisions. Accelerating adoption requires attention to the full continuum of translational research: developing new approaches, demonstrating their usefulness, disseminating and implementing findings, while engaging patients throughout the process. This encompasses basic science, preclinical and clinical research and implementation into practice, ultimately improving health. This article examines challenges to the adoption of precision medicine in the context of multimorbidity. Although the potential of precision medicine is enormous, proactive efforts are needed to avoid unintended consequences and foster its equitable and effective adoption.

ABSTRACTBackgroundSurvey data are essential in paediatric epidemiology, providing valuable insights into child health outcomes. The potential outcomes framework has advanced causal inference using observational data. However, traditional design‐based adjustments, especially sample weights, are often overlooked. This omission limits the ability to generalise findings to the broader population.ObjectiveThis study demonstrates three approaches for estimating the population average treatment effect (PATE) in a practical example, examining the impact of household second‐hand smoke (SHS) exposure on blood pressure in school‐aged children.MethodsUsing data from the National Health and Nutrition Examination Survey (NHANES) 2017–2020, we assessed the effect of household SHS exposure, a non‐randomised treatment, on blood pressure in school‐aged children. We applied estimators based on Inverse Probability of Treatment Weighting (IPTW), G‐computation, Targeted Maximum Likelihood Estimation (TMLE), and regression adjustment. Models without adjustments were run for comparison. We examined point estimates and the efficiency of the estimates obtained from these methods.ResultsThe largest differences were observed between the unadjusted regression models and the fully adjusted methods (IPTW, G‐computation, and TMLE), which account for both confounding and survey weights. While the inclusion of the sample weights leads to wider confidence intervals for all methods, G‐computation and TMLE showed comparatively narrower confidence intervals. Confidence intervals for the models not adjusted for sample weights were likely underestimated.ConclusionsThis study highlights the important role of sample weights in causal inference. Generalisability of the average treatment effect as estimated on data sampled using common survey designs to a defined population requires the use of sample weights. The estimators described provide a framework for incorporating sample weights, and their use in health research is recommended.

Objective: Address recent advances in precision medicine in the diagnosis and treatment of monogenic diabetes, including MODY, neonatal diabetes and rare forms.Method: The report was prepared between April and May 2023, following the PVO strategy.A search was performed in the PubMed database, using descriptors such as "Monogenic Diabetes", "MODY", "Diabetes Maturity Onset of the Young", "Neonatal Diabetes" and "Precision Medicine".Sixteen articles selected after inclusion and exclusion criteria were analyzed, of the 88 found.Results: Precision medicine is revolutionizing the treatment of single-gene diabetes, identifying specific mutations and providing more effective therapies.Lifestyle and dietary modifications are recommended for HNF1A-MODY, and the use of sulfonylureas may be considered.In the case of NDM, it is possible to transition from insulin to sulfonylureas.In insulin-resistant diabetes, the use of insulin-sensitizing agents is preferred.Early diagnosis and insulin treatment are key for LADA.Precision medicine improves the quality of life for patients with monogenic diabetes.Final considerations: Precision medicine has brought significant advances in the diagnosis and treatment of monogenic diabetes, providing more effective therapies and better quality of life for affected patients.These advances represent an important step towards personalized medicine for singlegene diabetes.

Chronic obstructive pulmonary disease (COPD) is a progressive lung disorder characterized by airflow limitation and persistent respiratory symptoms. Early COPD is gaining attention as an opportunity for early intervention, which may alter disease trajectory and improve patient outcomes. Early-stage COPD significantly influences the future progression and severity of the disease. Early COPD represents the initial stages of lung function decline, often before significant airflow obstruction is detected by spirometry. The progression of early COPD is influenced by genetic predisposition, environmental exposures, and individual susceptibility. Research indicates that early identification and intervention are critical in slowing disease progression and improving long-term outcomes. Early intervention in COPD through smoking cessation, tailored pharmacotherapy, pulmonary rehabilitation, and lifestyle changes is vital. These strategies collectively aim to slow disease progression, minimize symptoms, and improve patients’ quality of life. The landscape of early COPD management is evolving rapidly with advances in precision medicine, regenerative therapies, and novel pharmacological interventions. Future research and clinical trials will be essential in translating these innovations into effective treatments. By leveraging these advancements, clinicians can better tailor interventions to individual patients, ultimately improving outcomes and altering the trajectory of COPD progression. This review discusses the pathophysiology, risk factors, diagnostic criteria, and management strategies of early COPD, as well as recent advancements in the field. Early detection and intervention strategies, including smoking cessation, pharmacological therapy, and lifestyle modifications, are explored to highlight their significance in disease progression.

In Western societies, Alzheimer's disease (AD) is the most common form of dementia and the sixth leading cause of death. In recent years, the concept of precision medicine, an approach for disease prevention and treatment that is personalized to an individual's specific pattern of genetic variability, environment and lifestyle factors, has emerged. While for some diseases, in particular select cancers and a few monogenetic disorders such as cystic fibrosis, significant advances in precision medicine have been made over the past years, for most other diseases precision medicine is only in its beginning. To advance the application of precision medicine to a wider spectrum of disorders, governments around the world are starting to launch Precision Medicine Initiatives, major efforts to generate the extensive scientific knowledge needed to integrate the model of precision medicine into every day clinical practice. In this article we summarize the state of precision medicine in AD, review major obstacles in its development, and discuss its benefits in this highly prevalent, clinically and pathologically complex disease.