Results 31 to 40 of about 472,976 (271)
PLoS ONE, 2023 Recently, the utilization of real-world medical data collected from clinical sites has been attracting attention. Especially as the number of variables in real-world medical data increases, causal discovery becomes more and more effective.
Hideaki Kawaguchi
doaj +1 more source
Causal-learn: Causal Discovery in Python
, 2023 Causal discovery aims at revealing causal relations from observational data, which is a fundamental task in science and engineering. We describe $\textit{causal-learn}$, an open-source Python library for causal discovery. This library focuses on bringing a comprehensive collection of causal discovery methods to both practitioners and researchers.
Zheng, Yujia +8 more
openaire +2 more sources
Exposing the Probabilistic Causal Structure of Discrimination [PDF]
, 2017 Discrimination discovery from data is an important task aiming at identifying patterns of illegal and unethical discriminatory activities against protected-by-law groups, e.g., ethnic minorities.
Bonchi, Francesco +3 more
core +1 more source
A quantum causal discovery algorithm
, 2018 Finding a causal model for a set of classical variables is now a well-established task---but what about the quantum equivalent? Even the notion of a quantum causal model is controversial. Here, we present a causal discovery algorithm for quantum systems.
Costa, Fabio, Giarmatzi, Christina
core +2 more sources
On Incorporating Prior Knowledge Extracted From Large Language Models Into Causal Discovery
IEEE AccessLarge Language Models (LLMs) can reason about causality by leveraging vast pre-trained knowledge and text descriptions of datasets, demonstrating their effectiveness even when data is scarce.
Chanhui Lee +12 more
doaj +1 more source
Causality discovery technology
The European Physical Journal Special Topics, 2012 Causality is the fabric of our dynamic world. We all make frequent attempts to reason causation relationships of everyday events (e.g., what was the cause of my headache, or what has upset Alice?). We attempt to manage causality all the time through planning and scheduling.
Chen, M +6 more
openaire +2 more sources
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
Pediatric Blood &Cancer, EarlyView.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