GitHub

Applied Causal Inference with R

This workshop offers a hands-on introduction for social scientists seeking to apply causal inference methods to observational and secondary data. Following a recap of foundational concepts of causal ...
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

Detecting Root Causes for Process Performance Anomalies Using Causal Inference

Abstract: Process execution time is a key performance indicator for evaluating bottlenecks in business processes. Cases and activities that exceed the specified time constraints can be seen as ...
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 ...
IEEE

CausalSynG: Multivariate Collaborative Causal Inference With Dynamic Knowledge-Data Synergy for Industrial Soft Sensors

Abstract: Data-driven soft sensor techniques are increasingly being applied in complex industrial environments, enabling the modeling of many previously intractable variables and playing a critical ...
Scientific Research Publishing

Van der Laan, M.J. and Rose, S. (2011) Targeted Learning: Causal Inference for Observational and Experimental Data (pp. 89-135, 165-210). Springer.

ABSTRACT: Determining the causal effect of special education is a critical topic when making educational policy that focuses on student achievement. However, current special education research is ...
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 ...
jech.bmj

Causal mediation approaches for understanding pathways to inequalities and policy entry points: examples from early years health and development

The reduction of health inequalities has been a priority of researchers, decision-makers and practitioners for many years. Advances in causal mediation analysis offer great promise for identifying ...
publichealth.jhu

The promise and pitfalls of integrating data for causal inference, with application to mental health treatment

Please join the Department of Epidemiology Center for Clinical Trials and Evidence Synthesis (CCTES) and Center for Drug Safety and Effectiveness (CDSE) in welcoming Elizabeth Stuart, PhD, AM, Chair ...
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 ...