IEEE

A Knowledge-Based Hierarchical Causal Inference Network for Video Action Recognition

Abstract: Currently, existing action recognition methods mainly use a data-driven method to extract spatio-temporal representations of actions for recognition. However, this method may face ...
IEEE

Causal Inference in Geoscience and Remote Sensing From Observational Data

Abstract: Establishing causal relations between random variables from observational data is perhaps the most important challenge in today's science. In remote sensing and geosciences, this is of ...
UCL

Causal Inference in Practice

Causal inference is one of the most important and challenging aims in statistics and data science. Many fields, from clinical medicine to social sciences, strive to use empirical data to understand ...
Microsoft

Causal Inference Using LLM-Guided Discovery

At the core of causal inference lies the challenge of determining reliable causal graphs solely based on observational data. Since the well-known backdoor criterion depends on the graph, any errors in ...
Nature

Causal Python

The Nature Index tracks primary research articles from 145 natural-science and health-science journals, chosen based on reputation by an independent group of researchers. The Nature Index provides ...
Europe PMC

BCI Toolbox: An open-source python package for the Bayesian causal inference model.

Psychological and neuroscientific research over the past two decades has shown that the Bayesian causal inference (BCI) is a potential unifying theory that can account for a wide range of perceptual ...