Results 41 to 50 of about 1,819,138 (329)
Local Causal Discovery for Estimating Causal Effects
CoRR, 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.
Shantanu Gupta +2 more
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The KDD 2022 Workshop on Causal Discovery (CD2022)
, 2022 Causal relationships have been utilized in almost all disciplines, and the research into causal discovery has attracted a lot of attention in the last few years.
Emre Kiciman +9 more
core +1 more source
Differentiable Causal Backdoor Discovery
CoRR, 2020 Discovering the causal effect of a decision is critical to nearly all forms of decision-making. In particular, it is a key quantity in drug development, in crafting government policy, and when implementing a real-world machine learning system. Given only observational data, confounders often obscure the true causal effect. Luckily, in some cases, it is
Gultchin, L +3 more
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Testability of Instrumental Variables in Linear Non-Gaussian Acyclic Causal Models
Entropy, 2022 This paper investigates the problem of selecting instrumental variables relative to a target causal influence X→Y from observational data generated by linear non-Gaussian acyclic causal models in the presence of unmeasured confounders.
Feng Xie +5 more
doaj +1 more source
Causal-Discovery Performance of ChatGPT in the context of Neuropathic Pain Diagnosis [PDF]
arXiv.org, 2023 ChatGPT has demonstrated exceptional proficiency in natural language conversation, e.g., it can answer a wide range of questions while no previous large language models can.
Ruibo Tu, Chao Ma, Cheng Zhang
semanticscholar +1 more source
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
Disentangling causality: assumptions in causal discovery and inference
Artificial Intelligence Review, 2022 Abstract Causality has been a burgeoning field of research leading to the point where the literature abounds with different components addressing distinct parts of causality. For researchers, it has been increasingly difficult to discern the assumptions they have to abide by in order to glean sound conclusions from causal concepts or methods ...
Vonk, M.C. +3 more
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RealTCD: Temporal Causal Discovery from Interventional Data with Large Language Model [PDF]
International Conference on Information and Knowledge ManagementIn the field of Artificial Intelligence for Information Technology Operations, causal discovery is pivotal for operation and maintenance of systems, facilitating downstream industrial tasks such as root cause analysis.
Peiwen Li +8 more
semanticscholar +1 more source
Argumentation for Interactive Causal Discovery
Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, 2023 Causal reasoning reflects how humans perceive events in the world and establish relationships among them, identifying some as causes and others as effects. Causal discovery is about agreeing on these relationships and drawing them as a causal graph.
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Causal discovery with a mixture of DAGs
Machine Learning, 2022 Causal processes in biomedicine may contain cycles, evolve over time or differ between populations. However, many graphical models cannot accommodate these conditions. We propose to model causation using a mixture of directed cyclic graphs (DAGs), where the joint distribution in a population follows a DAG at any single point in time but potentially ...
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