arXiv (All)
open access
Simple Specification-Free Causal Inference in Large Panels
We propose a simple and transparent approach to causal inference using panel data with a large number of units and periods. Instead of imposing a particular parametric specification on the untreated potential outcomes, we assume they follow a nonparametric nonseparable factor model. We use long pre-
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
- On the Identifiability of Mixed Ordinal and Exponential Family Causal DAGs under Linear Parametric Models#1011654
- Testing selection on observables in parametric models with refreshment samples#999289
- Quantitative comparison of causal inference methods for climate tipping points#454369
- Optimal testing in linear models with implications for causal inference#1117210
- Certifying Interventional Agreement Among Observationally Equivalent Causal Models#1116578
Record · ID 1130153
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