# TSLiNGAM: DirectLiNGAM under heavy tails

arXiv: https://arxiv.org/abs/2308.05422 · HTML: https://mlsanity.com/paper/2308.05422/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 56 references that are in the map.
- First posted on arXiv 10 Aug 2023.

## Builds on (most cited first, 11 of 11)

- [CAM: Causal additive models, high-dimensional order search and penalized regression](https://mlsanity.com/paper/1310.1533/) (2013)
- [ParceLiNGAM: A causal ordering method robust against latent confounders](https://mlsanity.com/paper/1303.7410/) (2013)
- [Causal discovery in heavy-tailed models](https://mlsanity.com/paper/1908.05097/) (2019)
- [Independence Testing-Based Approach to Causal Discovery under Measurement Error and Linear Non-Gaussian Models](https://mlsanity.com/paper/2210.11021/) (2022)
- [A Distribution Free Conditional Independence Test with Applications to Causal Discovery](https://mlsanity.com/paper/2105.07555/) (2021)
- [A fast algorithm for computing distance correlation](https://mlsanity.com/paper/1810.11332/) (2018)
- [Estimation and Inference of Extremal Quantile Treatment Effects for Heavy-Tailed Distributions](https://mlsanity.com/paper/2110.06627/) (2021)
- [A direct method for estimating a causal ordering in a linear non-Gaussian acyclic model](https://mlsanity.com/paper/1408.2038/) (2014)
- [Causal Modelling of Heavy-Tailed Variables and Confounders with Application to River Flow](https://mlsanity.com/paper/2110.06686/) (2021)
- [Tail dependence of recursive max-linear models with regularly varying noise variables](https://mlsanity.com/paper/1701.07351/) (2017)
- [Causal Analysis at Extreme Quantiles with Application to London Traffic Flow Data](https://mlsanity.com/paper/2108.10215/) (2021)

## Abstract (arXiv)

One of the established approaches to causal discovery consists of combining directed acyclic graphs (DAGs) with structural causal models (SCMs) to describe the functional dependencies of effects on their causes. Possible identifiability of SCMs given data depends on assumptions made on the noise variables and the functional classes in the SCM. For instance, in the LiNGAM model, the functional class is restricted to linear functions and the disturbances have to be non-Gaussian. In this work, we propose TSLiNGAM, a new method for identifying the DAG of a causal model based on observational data. TSLiNGAM builds on DirectLiNGAM, a popular algorithm which uses simple OLS regression for identifying causal directions between variables. TSLiNGAM leverages the non-Gaussianity assumption of the error terms in the LiNGAM model to obtain more efficient and robust estimation of the causal structure. TSLiNGAM is justified theoretically and is studied empirically in an extensive simulation study. It performs significantly better on heavy-tailed and skewed data and demonstrates a high small-sample efficiency. In addition, TSLiNGAM also shows better robustness properties as it is more resilient to contamination.