arXiv (All)
open access
Causal Inference in Possibly Nonlinear Factor Models
This paper develops a causal inference method for treatment effects models with noisily measured confounders. The key feature is that a large number of noisy proxies are available and linked with the underlying latent confounders through an unknown, possibly nonlinear factor structure. The main buil
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
- Neuro-Causal Factor Analysis#665428
- Neuro-Causal Factor Analysis#663928
- Causal Discovery in Linear Models with Unobserved Variables and Measurement Error#128301
- CAER: Causal Action Effect Reweighting for World Model Training#622578
- Manipulating Causal Priors Affects the Outcome-Density Bias#263544
Record · ID 1143549
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