Tech Times

LLM Data Mixture Breaks When Training Pools Shift: Causal Inference Offers Fix

LLM training data mixture optimization breaks when training pools shift — every prior proxy experiment becomes stale.
GitHub

Tigramite – Causal inference for time series datasets

Update: Tigramite now has a new CausalEffects class that allows to estimate (conditional) causal effects and mediation based on assuming a causal graph. Have a look at the tutorial. Further, Tigramite ...
Scientific Research Publishing

Dynamic Causal Relationships in Stock Market: A Three-Dimensional Granger Causality Network Approach ()

1 Faculty of Science and Technology, University of Macau, Macau SAR, China. 2 School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan, China. 3 School of ...
GitHub

Imprecise Probabilistic Machine Learning

“There are known knowns. There are known unknowns. But there are also unknown unknowns—things we do not yet realize we do not know.”—Donald Rumsfeld (2002) While modern machine learning (ML) ...
Frontiers

A guide to bayesian networks software for structure and parameter learning, with a focus on causal discovery tools

A representation of the cause-effect mechanism is needed to enable artificial intelligence to represent how the world works. Bayesian Networks (BNs) have proven to be an effective and versatile tool ...
ascopubs.org

Clarifying Causal Effects of Interest and Underlying Assumptions in Randomized and Nonrandomized Clinical Trials in Oncology Using Directed Acyclic Graphs and Single-World ...

The aim of this research therefore was to streamline the understanding of typical causal structures in both randomized and nonrandomized clinical trials in oncology, presenting concise guidelines for ...