Efficient Causal Unit Selection using Tractable Arithmetic Circuits
The core message of this paper is that causal unit selection, which aims to find objects that optimize a causal objective function, can be efficiently solved using tractable arithmetic circuits (ACs) in linear time, in contrast to the exponential time required by the state-of-the-art variable elimination approach.
Evaluating the Ability of Large Language Models to Accurately Predict the Effects of Interventions on Causal Relationships
Large language models can demonstrate promising accuracy at predicting how causal relationships change under interventions, but their performance is sensitive to the presence of potentially memorized causal facts in the prompts.
A Dynamical View of Causal Reasoning in Multivariate Time Series Data Generated by Stochastic Processes
The author proposes a learning paradigm to establish causation between events in time series data, offering formal and computational tools for uncovering and quantifying causal relationships. The approach reframes causation as a machine learning problem using raw observational data.
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