arXiv (All)
open access
Robustness of Median-based Splitting Rules for Causal Trees
Causal forests (Athey and Imbens, 2016) are a widely used method for the estimation of heterogeneous treatment effects. Standard implementations use mean-based splitting criteria, which perform well when the outcome distribution is well-behaved and free of contamination, such as outliers or heavy ta
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Median-based Splitting Rules for Causal Trees and Forests#985299
- Mapping and Advancing the Scalability-Accuracy Frontier of Nonlinear Causal Discovery#1187936
- The Partial Linear Model in Causal Inference: Estimation, Selection, and Inference Leveraging Machine Learning#1405979
- Federated causal discovery in medicine: trends, opportunities, and challenges.#421055
- A Causal Inference Protocol for Root-cause Diagnosis in Non-stationary Industrial Time Series#1415996
Record · ID 1535899
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive