GitHub

StatsPAI: The Agent-Native Causal Inference & Econometrics Toolkit for Python

StatsPAI is the agent-native Python package for causal inference and applied econometrics. One import, 800+ functions, covering the complete empirical research workflow — from classical econometrics ...
CNBC

Intel's stock soars 20% as results top estimates, with chipmaker showing signs of growth

Intel reported first-quarter earnings that beat Wall Street expectations Revenue rose more than 7%, a sign that the chipmaker is finally starting to see some growth. The stock has been a Wall Street ...
GitHub

Causal Inference and Discovery in Python

What is this book about? Causal methods present unique challenges compared to traditional machine learning and statistics. Learning causality can be challenging, but it offers distinct advantages that ...
Semiconductor Engineering

Causal Inference for AMS Design (U. of Florida)

A new technical paper, “Causal AI For AMS Circuit Design: Interpretable Parameter Effects Analysis,” was published by the University of Florida. “Analog-mixed-signal (AMS) circuits are highly ...
IEEE

Variational GAN Enhanced Causal Effect Inference for Interpretable Learning in Heterogeneous IoT Systems

Abstract: While data-driven learning techniques are revolutionizing the smart application design and development, fundamental issues of lacking generalizability and interpretability are posing great ...
jech.bmj

Descriptive estimands, causal estimands, and avoiding the jungle of adjusted associations ‘in between’

2 Max Planck - University of Helsinki Center for Social Inequalities in Population Health, Helsinki, Finland Be upfront about the research study’s intention (this should link directly to the aim) - is ...
Frontiers

Augmenting large language models with psychologically grounded models of causal reasoning for planning under uncertainty

Large Language Models (LLMs) have come a long way in their ability to solve a wide range of problems. Yet, LLM decision-making still relies primarily on pattern recognition, which may limit its ...
IEEE

Direct Causal Inference for Out-of-Distribution Node Classification in Internet of Things

Abstract: Graph neural networks (GNNs) have achieved remarkable success in node classification tasks, yet their performance significantly degrades when encountering out-of-distribution (OOD) data due ...