Bayesian Modeling and Causal Inference
377 papers on this topic
Causal Parrots: Large Language Models May Talk Causality But Are Not Causal
Some argue scale is all what is needed to achieve AI, covering even causal models. We make it clear that large language models (LLMs) cannot be causal and give reason onto why sometimes we might...
Robust agents learn causal world models
It has long been hypothesised that causal reasoning plays a fundamental role in robust and general intelligence. However, it is not known if agents must learn causal models in order to generalise...
ALCM: Autonomous LLM-Augmented Causal Discovery Framework
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....
Amortized In-Context Bayesian Posterior Estimation
Bayesian inference provides a natural way of incorporating prior beliefs and assigning a probability measure to the space of hypotheses. Current solutions rely on iterative routines like Markov...
Large Language Models are Effective Priors for Causal Graph Discovery
Causal structure discovery from observations can be improved by integrating background knowledge provided by an expert to reduce the hypothesis space. Recently, Large Language Models (LLMs) have...
Exploring Causal Learning through Graph Neural Networks: An In-depth Review
In machine learning, exploring data correlations to predict outcomes is a fundamental task. Recognizing causal relationships embedded within data is pivotal for a comprehensive understanding of...
Causal Abstractions of Neural Networks
Structural analysis methods (e.g., probing and feature attribution) are increasingly important tools for neural network analysis. We propose a new structural analysis method grounded in a formal...
A Semantic Generalization of Shannon's Information Theory and Applications
Does semantic communication require a semantic information theory parallel to Shannon's information theory, or can Shannon's work be generalized for semantic communication? This paper advocates...
A Survey on Causal Discovery Methods for I.I.D. and Time Series Data
The ability to understand causality from data is one of the major milestones of human-level intelligence. Causal Discovery (CD) algorithms can identify the cause-effect relationships among the...
Relaxing partition admissibility in Cluster-DAGs: a causal calculus with arbitrary variable clustering
Cluster DAGs (C-DAGs) provide an abstraction of causal graphs in which nodes represent clusters of variables, and edges encode both cluster-level causal relationships and dependencies arisen from...
Do Large Language Models Reason Causally Like Us? Even Better?
Causal reasoning is a core component of intelligence. Large language models (LLMs) have shown impressive capabilities in generating human-like text, raising questions about whether their responses...
Variational Annealing on Graphs for Combinatorial Optimization
Several recent unsupervised learning methods use probabilistic approaches to solve combinatorial optimization (CO) problems based on the assumption of statistically independent solution variables....
Diffusion Models for Causal Discovery via Topological Ordering
Discovering causal relations from observational data becomes possible with additional assumptions such as considering the functional relations to be constrained as nonlinear with additive noise...
Temporal Latent Variable Structural Causal Model for Causal Discovery under External Interferences
Inferring causal relationships from observed data is an important task, yet it becomes challenging when the data is subject to various external interferences. Most of these interferences are the...
A Causal World Model Underlying Next Token Prediction: Exploring GPT in a Controlled Environment
Are generative pre-trained transformer (GPT) models, trained only to predict the next token, implicitly learning a world model from which sequences are generated one token at a time? We address...
Sanity Checking Causal Representation Learning on a Simple Real-World System
We evaluate methods for causal representation learning (CRL) on a simple, real-world system that satisfies the basic problem setup of CRL. The system consists of a controlled optical experiment...
DoWhy-GCM: An extension of DoWhy for causal inference in graphical causal models
We present DoWhy-GCM, an extension of the DoWhy Python library, which leverages graphical causal models. Unlike existing causality libraries, which mainly focus on effect estimation, DoWhy-GCM...
Learn to Accumulate Evidence from All Training Samples: Theory and Practice
Evidential deep learning, built upon belief theory and subjective logic, offers a principled and computationally efficient way to turn a deterministic neural network uncertainty-aware. The...