PsyPost

Correlation vs causation: what they mean and why the difference matters

Stop falling for misleading headlines. Understand the difference between correlation and causation, and learn how researchers prove real scientific facts.
publichealth.jhu

Biostatistics Dept. Seminar | Space-Time-Meta Causal Inference: A Weighting Perspective

Abstract: Causal inference with spatial, temporal, and meta-analytic data commonly defaults to regression modeling. While widely accepted, such regression approaches can suffer from model ...
IEEE

CiiNet: Self-Iterative Performance Optimization for Dynamic Networks Based on Causal Inference and Interpretable Evaluation

Abstract: Causal inference and root cause analysis play a crucial role in network performance evaluation and optimization by identifying critical parameters and explaining how the configuration ...
IEEE

Causal Inference Via Style Bias Deconfounding for Domain Generalization

Abstract: Deep neural networks (DNNs) often struggle with out-of-distribution data, limiting their reliability in real-world visual applications. To address this issue, domain generalization methods ...
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 ...
eLife

Correlation detection as a stimulus computable account for audiovisual perception, causal inference, and saliency maps in mammals

Animals excel at seamlessly integrating information from different senses, a capability critical for navigating complex environments. Despite recent progress in multisensory research, the absence of ...
Frontiers

Urban mobility and crime: causal inference using street closures as an instrumental variable

The advent of widely available cell phone mobility data in the United States has rapidly expanded the study of everyday mobility patterns in social science research. A wide range of existing ...
marktechpost

Meet oLLM: A Lightweight Python Library that brings 100K-Context LLM Inference to 8 GB Consumer GPUs via SSD Offload—No Quantization Required

oLLM is a lightweight Python library built on top of Huggingface Transformers and PyTorch and runs large-context Transformers on NVIDIA GPUs by aggressively offloading weights and KV-cache to fast ...
publichealth.jhu

Fundamentals of Causal Inference

Please join the JHU CFAR Biostatistics and Epidemiology Methodology (BEM) Core on Thursday, September 4, 2025, from 2-3 pm ET for a session covering the fundamentals of causal inference. If you have ...