Tootfinder

Narrating Causal Graphs with Large Language Models
Atharva Phatak, Vijay K. Mago, Ameeta Agrawal, Aravind Inbasekaran, Philippe J. Giabbanelli
https://arxiv.org/abs/2403.07118

Narrating Causal Graphs with Large Language Models
The use of generative AI to create text descriptions from graphs has mostly focused on knowledge graphs, which connect concepts using facts. In this work we explore the capability of large pretrained language models to generate text from causal graphs, where salient concepts are represented as nodes and causality is represented via directed, typed edges. The causal reasoning encoded in these graphs can support applications as diverse as healthcare or marketing. Using two publicly available caus…

Narrating Causal Graphs with Large Language Models
Atharva Phatak, Vijay K. Mago, Ameeta Agrawal, Aravind Inbasekaran, Philippe J. Giabbanelli
https://arxiv.org/abs/2403.07118

Narrating Causal Graphs with Large Language Models
The use of generative AI to create text descriptions from graphs has mostly focused on knowledge graphs, which connect concepts using facts. In this work we explore the capability of large pretrained language models to generate text from causal graphs, where salient concepts are represented as nodes and causality is represented via directed, typed edges. The causal reasoning encoded in these graphs can support applications as diverse as healthcare or marketing. Using two publicly available caus…

This https://arxiv.org/abs/2311.06458 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

Conditional Adjustment in a Markov Equivalence Class
We consider the problem of identifying a conditional causal effect through covariate adjustment. We focus on the setting where the causal graph is known up to one of two types of graphs: a maximally oriented partially directed acyclic graph (MPDAG) or a partial ancestral graph (PAG). Both MPDAGs and PAGs represent equivalence classes of possible underlying causal models. After defining adjustment sets in this setting, we provide a necessary and sufficient graphical criterion -- the conditional …

Learning causal graphs using variable grouping according to ancestral relationship
Ming Cai, Hisayuki Hara
https://arxiv.org/abs/2403.14125 https://…

Learning causal graphs using variable grouping according to ancestral relationship
Several causal discovery algorithms have been proposed. However, when the sample size is small relative to the number of variables, the accuracy of estimating causal graphs using existing methods decreases. And some methods are not feasible when the sample size is smaller than the number of variables. To circumvent these problems, some researchers proposed causal structure learning algorithms using divide-and-conquer approaches. For learning the entire causal graph, the approaches first split v…

Automating Psychological Hypothesis Generation with AI: Large Language Models Meet Causal Graph
Song Tong, Kai Mao, Zhen Huang, Yukun Zhao, Kaiping Peng
https://arxiv.org/abs/2402.14424

Automating Psychological Hypothesis Generation with AI: Large Language Models Meet Causal Graph
Leveraging the synergy between causal knowledge graphs and a large language model (LLM), our study introduces a groundbreaking approach for computational hypothesis generation in psychology. We analyzed 43,312 psychology articles using a LLM to extract causal relation pairs. This analysis produced a specialized causal graph for psychology. Applying link prediction algorithms, we generated 130 potential psychological hypotheses focusing on `well-being', then compared them against research ideas …

Applied Causal Inference Powered by ML and AI
Victor Chernozhukov, Christian Hansen, Nathan Kallus, Martin Spindler, Vasilis Syrgkanis
https://arxiv.org/abs/2403.02467

Applied Causal Inference Powered by ML and AI
An introduction to the emerging fusion of machine learning and causal inference. The book presents ideas from classical structural equation models (SEMs) and their modern AI equivalent, directed acyclical graphs (DAGs) and structural causal models (SCMs), and covers Double/Debiased Machine Learning methods to do inference in such models using modern predictive tools.

This https://arxiv.org/abs/2108.03018 has been replaced.
link: https://scholar.google.com/scholar?q=a

Conditional Separation as a Binary Relation. A Coq Assisted Proof
The concept of d-separation holds a pivotal role in causality theory, serving as a fundamental tool for deriving conditional independence properties from causal graphs. Pearl defined the d-separation of two subsets conditionally on a third one. In this study, we present a novel perspective by showing i) how the d-separation can be extended beyond acyclic graphs, possibly infinite, and ii) how it can be expressed and characterized as a binary relation between vertices. Compared to the typ…

This https://arxiv.org/abs/2310.14691 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Identifiability of total effects from abstractions of time series causal graphs
We study the problem of identifiability of the total effect of an intervention from observational time series only given an abstraction of the causal graph of the system. Specifically, we consider two types of abstractions: the extended summary causal graph which conflates all lagged causal relations but distinguishes between lagged and instantaneous relations; and the summary causal graph which does not give any indication about the lag between causal relations. We show that the total effect i…

Bayesian Graphs of Intelligent Causation
Preetha Ramiah, James Q. Smith, Oliver Bunnin, Silvia Liverani, Jamie Addison, Annabel Whipp
https://arxiv.org/abs/2404.03957

Bayesian Graphs of Intelligent Causation
Probabilistic Graphical Bayesian models of causation have continued to impact on strategic analyses designed to help evaluate the efficacy of different interventions on systems. However, the standard causal algebras upon which these inferences are based typically assume that the intervened population does not react intelligently to frustrate an intervention. In an adversarial setting this is rarely an appropriate assumption. In this paper, we extend an established Bayesian methodology called Ad…

This https://arxiv.org/abs/2108.03018 has been replaced.
link: https://scholar.google.com/scholar?q=a

Conditional Separation as a Binary Relation. A Coq Assisted Proof
The concept of d-separation holds a pivotal role in causality theory, serving as a fundamental tool for deriving conditional independence properties from causal graphs. Pearl defined the d-separation of two subsets conditionally on a third one. In this study, we present a novel perspective by showing i) how the d-separation can be extended beyond acyclic graphs, possibly infinite, and ii) how it can be expressed and characterized as a binary relation between vertices. Compared to the typ…

This https://arxiv.org/abs/2108.03018 has been replaced.
link: https://scholar.google.com/scholar?q=a

Conditional Separation as a Binary Relation. A Coq Assisted Proof
The concept of d-separation holds a pivotal role in causality theory, serving as a fundamental tool for deriving conditional independence properties from causal graphs. Pearl defined the d-separation of two subsets conditionally on a third one. In this study, we present a novel perspective by showing i) how the d-separation can be extended beyond acyclic graphs, possibly infinite, and ii) how it can be expressed and characterized as a binary relation between vertices. Compared to the typ…

This https://arxiv.org/abs/2209.06704 has been replaced.
link: https://scholar.google.com/scholar?q=a

Causal chain event graphs for remedial maintenance
The analysis of system reliability has often benefited from graphical tools such as fault trees and Bayesian networks. In this article, instead of conventional graphical tools, we apply a probabilistic graphical model called the chain event graph (CEG) to represent the failures and processes of deterioration of a system. The CEG is derived from an event tree and can flexibly represent the unfolding of asymmetric processes. For this application we need to define a new class of formal interventio…

This https://arxiv.org/abs/2209.06704 has been replaced.
link: https://scholar.google.com/scholar?q=a

Causal chain event graphs for remedial maintenance
The analysis of system reliability has often benefited from graphical tools such as fault trees and Bayesian networks. In this article, instead of conventional graphical tools, we apply a probabilistic graphical model called the chain event graph (CEG) to represent the failures and processes of deterioration of a system. The CEG is derived from an event tree and can flexibly represent the unfolding of asymmetric processes. For this application we need to define a new class of formal interventio…

Adaptive Bayesian Structure Learning of DAGs With Non-conjugate Prior
S. Nazari, M. Arashi, A. Sadeghkhani
https://arxiv.org/abs/2403.17489 https://…

Adaptive Bayesian Structure Learning of DAGs With Non-conjugate Prior
Directed Acyclic Graphs (DAGs) are solid structures used to describe and infer the dependencies among variables in multivariate scenarios. Having a thorough comprehension of the accurate DAG-generating model is crucial for causal discovery and estimation. Our work suggests utilizing a non-conjugate prior for Gaussian DAG structure learning to enhance the posterior probability. We employ the idea of using the Bessel function to address the computational burden, providing faster MCMC computation …