Results 71 to 80 of about 2,139,585 (163)

Causal Agent based on Large Language Model [PDF]

arXiv
Large language models (LLMs) have achieved significant success across various domains. However, the inherent complexity of causal problems and causal theory poses challenges in accurately describing them in natural language, making it difficult for LLMs to comprehend and use them effectively.
arxiv  

Dagma-DCE: Interpretable, Non-Parametric Differentiable Causal Discovery

IEEE Open Journal of Signal Processing
We introduce Dagma-DCE, an interpretable and model-agnostic scheme for differentiable causal discovery. Current non- or over-parametric methods in differentiable causal discovery use opaque proxies of “independence” to justify the inclusion
Daniel Waxman, Kurt Butler, Petar M. Djuric   +2 more
doaj   +1 more source

Learning Linear Non-Gaussian Causal Models in the Presence of Latent Variables [PDF]

arXiv, 2019
We consider the problem of learning causal models from observational data generated by linear non-Gaussian acyclic causal models with latent variables. Without considering the effect of latent variables, one usually infers wrong causal relationships among the observed variables.
arxiv  

Identification of Causal Effects on Binary Outcomes Using Structural Mean Models [PDF]

Structural mean models (SMMs) are used to estimate causal effects among those selecting treatment in randomised controlled trials affected by non-ignorable non-compliance.
Frank Windmeijer, Paul Clarke
core  

Confounding caused by causal-effect covariability [PDF]

arXiv, 2018
Confounding seriously impairs our ability to learn about causal relations from observational data. Confounding can be defined as a statistical association between two variables due to inputs from a common source (the confounder). For example, if $Z\rightarrow Y$ and $Z\rightarrow X$, then $X$ and $Y$ will be statistically dependent, even if there are ...
arxiv  

Changes in Compulsory Schooling and the Causal Effect of Education on Health: Evidence from Germany [PDF]

In this paper we investigate the causal effect of years of schooling on health and health-related behavior in West Germany. We apply an instrumental variables approach using as natural experiments several changes in compulsory schooling laws between 1949
Jürges, Hendrik, Kemptner, Daniel, Reinhold, Steffen   +2 more
core  

Causal Fine-Tuning and Effect Calibration of Non-Causal Predictive Models [PDF]

arXiv
This paper proposes techniques to enhance the performance of non-causal models for causal inference using data from randomized experiments. In domains like advertising, customer retention, and precision medicine, non-causal models that predict outcomes under no intervention are often used to score individuals and rank them according to the expected ...
arxiv  

Unveiling and Causalizing CoT: A Causal Pespective [PDF]

arXiv
Although Chain-of-Thought (CoT) has achieved remarkable success in enhancing the reasoning ability of large language models (LLMs), the mechanism of CoT remains a ``black box''. Even if the correct answers can frequently be obtained, existing CoTs struggle to make the reasoning understandable to human.
arxiv  

Re-examining Granger Causality from Causal Bayesian Networks Perspective [PDF]

arXiv
Characterizing cause-effect relationships in complex systems could be critical to understanding these systems. For many, Granger causality (GC) remains a computational tool of choice to identify causal relations in time series data. Like other causal discovery tools, GC has limitations and has been criticized as a non-causal framework.
arxiv  

ALCM: Autonomous LLM-Augmented Causal Discovery Framework [PDF]

arXiv
To perform effective causal inference in high-dimensional datasets, initiating the process with causal discovery is imperative, wherein a causal graph is generated based on observational data. However, obtaining a complete and accurate causal graph poses a formidable challenge, recognized as an NP- hard problem.
arxiv