Results 51 to 60 of about 1,964,226 (308)
Causal Discovery Evaluation Framework in the Absence of Ground-Truth Causal Graph
IEEE AccessIn causal learning, discovering the causal graph of the underlying generative mechanism from observed data is crucial. However, real-world data for causal discovery is scarce and expensive, leading researchers to rely on synthetic datasets, which may not
Tingpeng Li +5 more
doaj +1 more source
Scientific Reports, 2017 Learning the causal relationships that define a molecular system allows us to predict how the system will respond to different interventions. Distinguishing causality from mere association typically requires randomized experiments. Methods for automated
Sofia Triantafillou +5 more
doaj +1 more source
Local Causal Discovery for Estimating Causal Effects
, 2023 Even when the causal graph underlying our data is unknown, we can use observational data to narrow down the possible values that an average treatment effect (ATE) can take by (1) identifying the graph up to a Markov equivalence class; and (2) estimating that ATE for each graph in the class.
Gupta, Shantanu +2 more
openaire +2 more sources
Sickle Cell Disease Is an Inherent Risk for Asthma in a Sibling Comparison Study
Pediatric Blood &Cancer, EarlyView.ABSTRACT Introduction Sickle cell disease (SCD) and asthma share a complex relationship. Although estimates vary, asthma prevalence in children with SCD is believed to be comparable to or higher than the general population. Determining whether SCD confers an increased risk for asthma remains challenging due to overlapping symptoms and the ...
Suhei C. Zuleta De Bernardis +9 more
wiley +1 more source
The role of assumptions in causal discovery [PDF]
, 2009 The paper looks at the conditional independence search approach to causal discovery, proposed by Spirtes et al. and Pearl and Verma, from the point of view of the mechanism-based view of causality in econometrics, explicated by Simon.
Druzdzel, Marek J
core
, 2022 It is crucial to consider the social and ethical consequences of AI and ML based decisions for the safe and acceptable use of these emerging technologies. Fairness, in particular, guarantees that the ML decisions do not result in discrimination against individuals or minorities.
Binkytė-Sadauskienė, Rūta +4 more
openaire +3 more sources
Pediatric Blood &Cancer, EarlyView.ABSTRACT Background/Objectives Osteosarcoma is a radioresistant tumor that may benefit from stereotactic body radiation therapy (SBRT) for locoregional control in metastatic/recurrent disease. We report institutional practice patterns, outcomes, toxicity, and failures in osteosarcoma patients treated with SBRT.
Jenna Kocsis +13 more
wiley +1 more source
Constraint-based Causal Discovery for Non-Linear Structural Causal Models with Cycles and Latent Confounders [PDF]
, 2018 We address the problem of causal discovery from data, making use of the recently proposed causal modeling framework of modular structural causal models (mSCM) to handle cycles, latent confounders and non-linearities.
Forré, Patrick, Mooij, Joris M.
core +1 more source
Lifestyle Behaviors and Cardiotoxic Treatment Risks in Adult Childhood Cancer Survivors
Pediatric Blood &Cancer, EarlyView.ABSTRACT Background Higher doses of anthracyclines and heart‐relevant radiotherapy increase cardiovascular disease (CVD) risk. This study assessed CVD and CVD risk factors among adult childhood cancer survivors (CCSs) across cardiotoxic treatment risk groups and examined associations between lifestyle behaviors and treatment risks.
Ruijie Li +6 more
wiley +1 more source
Bayesian causal discovery for policy decision making
Data & PolicyThis 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