Results 41 to 50 of about 477,715 (269)
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
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
Pediatric Blood &Cancer, EarlyView.ABSTRACT Objective To evaluate the diagnostic yield and utility of universal paired tumor–normal multigene panel sequencing in newly diagnosed pediatric solid and central nervous system (CNS) tumor patients and to compare the detection of germline pathogenic/likely pathogenic variants (PV/LPVs) against established clinical referral criteria for cancer ...
Natalie Waligorski +9 more
wiley +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
, 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 Purpose Although not always achieved, complete chemotherapy‐induced nausea and vomiting (CINV) control is the conventional goal of CINV prophylaxis. In this two‐center, mixed‐methods study, we sought to understand the preferences of adolescent patients and family caregivers for CINV control endpoints.
Haley Newman +8 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
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
Pediatric Blood &Cancer, EarlyView.ABSTRACT End‐of‐life conversations with adolescents and young adults (AYAs) with cancer rarely occur without the guidance of healthcare professionals. As a part of the ‘Difficult Discussions’ study, focused on palliative care and advance care planning discussions with AYAs with cancer, we investigated the factors that healthcare professionals identify ...
Justine Lee +9 more
wiley +1 more source
An Upper Bound for Random Measurement Error in Causal Discovery [PDF]
, 2018 Causal discovery algorithms infer causal relations from data based on several assumptions, including notably the absence of measurement error. However, this assumption is most likely violated in practical applications, which may result in erroneous ...
Blom, Tineke +3 more
core +1 more source