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 ...
KSL

Meta buried 'causal' evidence of social media harm, court filings allege

SAN FRANCISCO — Meta shut down internal research into the mental health effects of Facebook and Instagram after finding causal evidence that its products harmed users' mental health, according to ...
VentureBeat

Alembic melted GPUs chasing causal A.I. — now it's running one of the fastest supercomputers in the world

Alembic Technologies has raised $145 million in Series B and growth funding at a valuation 15 times higher than its previous round, betting that the next competitive advantage in artificial ...
eLife

Animacy semantic network supports causal inferences about illness

Inferring the causes of illness is a culturally universal example of causal thinking. We tested the hypothesis that making causal inferences about biological processes (e.g. illness) depends on the ...
Frontiers

Correlation does not equal causation: the imperative of causal inference in machine learning models for immunotherapy

1 Department of Orthopedics, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei, China 2 Department of Pharmacy, the Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei, ...
Frontiers

Causal ECGNet: leveraging causal inference for robust ECG classification in cardiac disorders

Electrocardiogram (ECG) is a graphical representation of the electrical activity of the heart and plays a crucial role in diagnosing heart disease and assessing cardiac function. In the context of ...
eLife

Animacy semantic network supports implicit causal inferences about illness

During the peer-review process the editor and reviewers write an eLife assessment that summarises the significance of the findings reported in the article (on a scale ranging from landmark to useful) ...
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.
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 ...
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 ...