arXiv (All)
open access
Conditional Independence Testing in Time Series
We consider the problem of testing Granger causality in time series, specifically, whether the future outcome $Y_{t+1}$ and the exposure history $\bar X_t$ are conditionally independent given the history of $\bar Y_t$ and a set of confounding variables $\bar Z_t$ up to time $t$. This testing procedu
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 testing for independence between generalized error models of several time series#139096
- Beyond Stationarity in Time Series: Discovering Causal Structures and Latent Regimes via Markov Blankets#660817
- Causal inference with Bayesian regression modeling: A tutorial#469735
- A new theory of causation based on probability distribution determinism#88141
- Generalized Covariance Estimator under Misspecification#999172
Record · ID 1034566
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