arXiv (All)
open access
Long Story Short: Omitted Variable Bias in Causal Machine Learning
We develop a general theory of omitted variable bias for a wide range of common causal parameters, including (but not limited to) averages of potential outcomes, average treatment effects, average causal derivatives, and policy effects from covariate shifts. Our theory applies to nonparametric model
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
- Causal Inference of Ordinal Outcomes: A Bayesian Solution#1070476
- Extrapolation of causal effects – hopes, assumptions, and the extrapolator’s circle#744656
- From Control to Treatment: Causal Forecasting Beyond the Observed Panel#819403
- From Control to Treatment: Causal Forecasting Beyond the Observed Panel#809410
- Flexible Nonparametric Inference for Causal Effects under the Front-Door Model#189176
Record · ID 1049549
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