# Consistency of Causal Inference under the Additive Noise Model

arXiv: https://arxiv.org/abs/1312.5770 · HTML: https://mlsanity.com/paper/1312.5770/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Dec 2013.

## Built on by (most cited first, 8 of 8)

- [Towards Causal Representation Learning](https://mlsanity.com/paper/2102.11107/) (2021)
- [On the Identifiability and Estimation of Causal Location-Scale Noise Models](https://mlsanity.com/paper/2210.09054/) (2022)
- [From Statistical to Causal Learning](https://mlsanity.com/paper/2204.00607/) (2022)
- [Entropic Causal Inference: Identifiability and Finite Sample Results](https://mlsanity.com/paper/2101.03501/) (2021)
- [Robust Estimation of Causal Heteroscedastic Noise Models](https://mlsanity.com/paper/2312.10102/) (2023)
- [Causal Identification with Additive Noise Models: Quantifying the Effect of Noise](https://mlsanity.com/paper/2110.08087/) (2021)
- [A Review and Roadmap of Deep Causal Model from Different Causal Structures and Representations](https://mlsanity.com/paper/2311.00923/) (2023)
- [The Effect of Noise Level on Causal Identification with Additive Noise Models](https://mlsanity.com/paper/2108.11320/) (2021)

## Abstract (arXiv)

We analyze a family of methods for statistical causal inference from sample under the so-called Additive Noise Model. While most work on the subject has concentrated on establishing the soundness of the Additive Noise Model, the statistical consistency of the resulting inference methods has received little attention. We derive general conditions under which the given family of inference methods consistently infers the causal direction in a nonparametric setting.