Covariate Selection for Doubly Robust Double/debiased Machine Learning Estimators for Causal Inference
This story is from 2026-09-16. It is preserved in the archive; the latest stories are on the live feed.
arXiv:2609.17238v1 Announce Type: cross Abstract: High-dimensional data create challenges for causal effect estimation because identifying the covariates needed for correct model specification becomes increasingly difficult. Double/debiased machine learning (DML) facilitates the use of machine lear…
Read the full story at arXiv stat.ML ↗
Timeline · 1 report
- 2026-09-16 04:00 · arXiv stat.ML
Covariate Selection for Doubly Robust Double/debiased Machine Learning Estimators for Causal Inference
More stories
- AI's role in building AI surging? Anthropic says Claude now leads 26% of its R&D — Mint AI
- Anthropic, OpenAI, SpaceXAI, Google sued over call to ‘pace’ AI development — Politico Technology
- Google Joins OpenAI, Anthropic, Meta in Disclosing AI Hacks — Bloomberg AI
- Introducing Kimi K3 on Amazon Bedrock — AWS Machine Learning Blog
- Optimizing agent system prompts with Amazon Bedrock AgentCore — AWS Machine Learning Blog
- Introducing Amazon SageMaker HyperPod Inference Gateway — AWS Machine Learning Blog
- Introducing Astra for Law — OpenAI News
- Novo Nordisk Will Use Anthropic’s Claude for Drug Research — Wall Street Journal Technology
Get the daily brief of stories like this at 6:30 every morning →