# Causal Independence for Knowledge Acquisition and Inference

arXiv: https://arxiv.org/abs/1303.1468 · HTML: https://mlsanity.com/paper/1303.1468/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Mar 2013.

## Built on by (most cited first, 4 of 4)

- [A Causal Approach for Unfair Edge Prioritization and Discrimination Removal](https://mlsanity.com/paper/2111.14348/) (2021)
- [Surjective Independence of Causal Influences for Local Bayesian Network Structures](https://mlsanity.com/paper/2509.24759/) (2025)
- [Structural Refinement of Bayesian Networks for Efficient Model Parameterisation](https://mlsanity.com/paper/2510.00334/) (2025)
- [Binary Verification for Zero-Shot Vision](https://mlsanity.com/paper/2511.10983/) (2025)

## Abstract (arXiv)

I introduce a temporal belief-network representation of causal independence that a knowledge engineer can use to elicit probabilistic models. Like the current, atemporal belief-network representation of causal independence, the new representation makes knowledge acquisition tractable. Unlike the atemproal representation, however, the temporal representation can simplify inference, and does not require the use of unobservable variables. The representation is less general than is the atemporal representation, but appears to be useful for many practical applications.