arXiv (All)
open access
Locally Robust Inference with Outcomes Predicted by Machine Learning
Many parameters of interest in economics depend on outcomes generated by machine learning (ML).These generated outcomes are subsequently used in standard econometric estimation and typically treated as observed, a practice that threatens the validity of downstream inference. Empirically relevant cas
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
- Robust Inference Via Heteroskedasticity in Linear Models#235419
- When the Martingale Never Stops Firing: Anytime-Valid Gating on Real Forecast Streams#799697
- Econometrics with Pre-Trained Embeddings for Unstructured Data#1105111
- Predictor Selection for Doubly Robust Double/debiased Machine Learning Estimators for Causal Inference#1374549
- Predictor Selection for Doubly Robust Double/debiased Machine Learning Estimators for Causal Inference#1374539
Record · ID 1357411
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