Quantifying Aleatoric Uncertainty of the Treatment Effect: A Novel Orthogonal Learner

Oct-10-2025, 15:18:21 GMT–Neural Information Processing Systems

Estimating causal quantities from observational data is crucial for decision-making in medicine [9, 12, 22, 30, 70]. For example, medical practitioners are interested in estimating the effect of chemotherapy vs. immunotherapy on patient survival from electronic health records to understand the best treatment

artificial intelligence, data mining, machine learning, (18 more...)

Neural Information Processing Systems

Oct-10-2025, 15:18:21 GMT

Conferences PDF

Add feedback

Country:
- Europe
  - Germany > Bavaria
    - Upper Bavaria > Munich (0.04)
  - Spain > Galicia
    - Madrid (0.04)
  - Sweden > Norrbotten County
    - Luleå (0.04)
  - United Kingdom > England
    - Cambridgeshire > Cambridge (0.14)
- North America > United States
  - California > Alameda County > Berkeley (0.04)

Genre:
- Research Report > Experimental Study (1.00)

Industry:
- Health & Medicine
  - Pharmaceuticals & Biotechnology (1.00)
  - Therapeutic Area > Oncology (1.00)

Technology:
- Information Technology
  - Artificial Intelligence
    - Machine Learning
      - Neural Networks (0.67)
      - Statistical Learning (1.00)
    - Representation & Reasoning (1.00)
  - Data Science > Data Mining (0.67)

Duplicate Docs Excel Report

Title
Quantifying Aleatoric Uncertainty of the Treatment Effect: A Novel Orthogonal Learner

Similar Docs Excel Report more

Title	Similarity	Source
None found