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Found 16 papers, 5 papers with code
Causal Parrots: Large Language Models May Talk Causality But Are Not Causal
We conjecture that in the cases where LLM succeed in doing causal inference, underlying was a respective meta SCM that exposed correlations between causal facts in natural language on whose data the LLM was ultimately trained.
Continual Causal Abstractions
This short paper discusses continually updated causal abstractions as a potential direction of future research.
Pearl Causal Hierarchy on Image Data: Intricacies & Challenges
Many researchers have voiced their support towards Pearl's counterfactual theory of causation as a stepping stone for AI/ML research's ultimate goal of intelligent systems.
On How AI Needs to Change to Advance the Science of Drug Discovery
Research around AI for Science has seen significant success since the rise of deep learning models over the past decade, even with longstanding challenges such as protein structure prediction.
Can Foundation Models Talk Causality?
Foundation models are subject to an ongoing heated debate, leaving open the question of progress towards AGI and dividing the community into two camps: the ones who see the arguably impressive results as evidence to the scaling hypothesis, and the others who are worried about the lack of interpretability and reasoning capabilities.
Towards a Solution to Bongard Problems: A Causal Approach
Even though AI has advanced rapidly in recent years displaying success in solving highly complex problems, the class of Bongard Problems (BPs) yet remain largely unsolved by modern ML techniques.
Tearing Apart NOTEARS: Controlling the Graph Prediction via Variance Manipulation
Simulations are ubiquitous in machine learning.
Machines Explaining Linear Programs
In this work, we extend the attribution methods for explaining neural networks to linear programs.
Attributions Beyond Neural Networks: The Linear Program Case
We introduce an approach where we consider neural encodings for LPs that justify the application of attribution methods from explainable artificial intelligence (XAI) designed for neural learning systems.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
Finding Structure and Causality in Linear Programs
Linear Programs (LP) are celebrated widely, particularly so in machine learning where they have allowed for effectively solving probabilistic inference tasks or imposing structure on end-to-end learning systems.
A Taxonomy for Inference in Causal Model Families
More specifically, there are models capable of answering causal queries that are not SCM, which we refer to as \emph{partially causal models} (PCM).
The Causal Loss: Driving Correlation to Imply Causation
Most algorithms in classical and contemporary machine learning focus on correlation-based dependence between features to drive performance.
Causal Explanations of Structural Causal Models
The question part on the user's end we believe to be solved since the user's mental model can provide the causal model.
Relating Graph Neural Networks to Structural Causal Models
Causality can be described in terms of a structural causal model (SCM) that carries information on the variables of interest and their mechanistic relations.
Structural Causal Models Reveal Confounder Bias in Linear Program Modelling
The recent years have been marked by extended research on adversarial attacks, especially on deep neural networks.
Interventional Sum-Product Networks: Causal Inference with Tractable Probabilistic Models
While probabilistic models are an important tool for studying causality, doing so suffers from the intractability of inference.
