This valuable study investigates the neural basis of causal inference of illness, suggesting that it relies on semantic networks specific to living things in the absence of a generalized ...

In this study, the authors aim to understand the neural basis of implicit causal inference, specifically how people ... and the manuscript is mostly focused on PC (also the abstract). To what extent ...

Learn More Lambda is a 12-year-old San Francisco company best known for offering graphics processing units ... further with the launch of the Lambda Inference API (application programming ...

Abstract: Latent confounders are a fundamental challenge for inferring causal effects from observational data. The instrumental variable (IV) approach is a practical way to address this challenge.

Causal inference aims to produce AI systems that operate better in the real world ... The problem grows worse as a model becomes more complex and situations become more abstract. “Very often an LLM ...

DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical ...

recognize the core relevance and challenges in drawing causal inferences from data; understand advantages, challenges, and limitations of experimental, quasi-experimental, and evaluation study designs ...