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 ...
note

Summary of the book 'Causal Inference and Discovery in Python'

This is a summary of '(Code Downloadable) Causal Inference and Discovery in Python [Concepts and Practice]: Unlocking the Key to Causal Machine Learning' (published August 20, 2024, by Aleksander ...
BMJ

Causal inference in physical activity epidemiology research: an opportunity for better evidence

Decades of research have established a significant link between physical activity and health, influencing agenda setting, policy making and community awareness.1–4 However, the field continues to ...
Frontiers

Designing digital health interventions with causal inference and multi-armed bandits: a review

Recent statistics from the World Health Organization show that non-communicable diseases account for 74% of global fatalities, with lifestyle playing a pivotal role in their development. Promoting ...
Microsoft

Causal Order: The Key to Leveraging Imperfect Experts in Causal Inference

Large Language Models (LLMs) have recently been used as experts to infer causal graphs, often by repeatedly applying a pairwise prompt that asks about the causal relationship of each variable pair.
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 ...
IEEE

Causal Inference-Based Large-Scale Multiobjective Optimization

Abstract: Large-scale multiobjective optimization problems (LSMOPs), characterized by a substantial number of decision variables, pose significant challenges for many existing evolutionary algorithms.
acm.org

Causal Inference Makes Sense of AI

Classical machine learning (ML) is remarkably effective at finding patterns and associations in data. It can spot correlations that escape human eyes and minds. Yet the technology suffers from a ...
Frontiers

CISepsis: a causal inference framework for early sepsis detection

The early prediction of sepsis based on machine learning or deep learning has achieved good results.Most of the methods use structured data stored in electronic medical records, but the pathological ...