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Adversarial balancing-based representation learning for causal effect inference with observational data [PDF]
Learning causal effects from observational data greatly benefits a variety of domains such as health care, education, and sociology. For instance, one could estimate the impact of a new drug on specific individuals to assist clinical planning and improve
Xin Du +4 more
semanticscholar +1 more source
Bayesian causal discovery for policy decision making
This paper demonstrates how learning the structure of a Bayesian network, often used to predict and represent causal pathways, can be used to inform policy decision-making.
Catarina Moreira +6 more
doaj +1 more source
Background: Body Mass Index (BMI) and maternal age are related to various disorders of the female reproductive system. This study aimed to estimate the causal effects of BMI and maternal age on the rate of metaphase II oocytes (MII) using a new ...
Ahad ALIZADEH +4 more
doaj +1 more source
Causal evidence is needed to act and it is often enough for the evidence to point towards a direction of the effect of an action. For example, policymakers might be interested in estimating the effect of slightly increasing taxes on private spending across the whole population.
Rothenhäusler, Dominik, Yu, Bin
openaire +2 more sources
ABSTRACT Introduction Characterizing stressful events reported by childhood cancer survivors experienced throughout the lifespan may help improve trauma‐informed care relevant to the survivor experience. Methods Participants included 2552 survivors (54% female; 34 years of age) and 469 community controls (62% female; 33 years of age) from the St.
Megan E. Ware +13 more
wiley +1 more source
Introduction Maternal smoking during pregnancy disturbs fetal lung development, and induces in their offspring childhood respiratory diseases. Whether it has a continued impact on offspring adult lung health and exerts a casual effect of chronic ...
Yun-Xia Huo +3 more
doaj +1 more source
Information Bottleneck for Estimating Treatment Effects with Systematically Missing Covariates
Estimating the effects of an intervention from high-dimensional observational data is a challenging problem due to the existence of confounding. The task is often further complicated in healthcare applications where a set of observations may be entirely ...
Sonali Parbhoo +3 more
doaj +1 more source
Abstract Background Sickle cell disease (SCD) is an autosomal recessive hemoglobinopathy affecting millions of individuals worldwide. The clinical expression and psychosocial burden of SCD vary widely across geographical, cultural, and healthcare system contexts, underscoring the need for setting‐specific approaches to assessment.
Desiré Fantasia +7 more
wiley +1 more source
BackgroundAn increasing body of evidence suggests a profound interrelation between the microbiome and sleep-related concerns. Nevertheless, current observational studies can merely establish their correlation, leaving causality unexplored.Study ...
Xiaoqiu Wang +5 more
doaj +1 more source
Influenza A virus (IAV) is a respiratory pathogen that causes substantial morbidity and mortality during both seasonal and pandemic outbreaks. Infection outcomes in unexposed populations are affected by host genetics, but the host genetic architecture is
Paul L. Maurizio +15 more
doaj +1 more source

