# Causal Effect Identification in LiNGAM Models with Latent Confounders

arXiv: https://arxiv.org/abs/2406.02049 · HTML: https://mlsanity.com/paper/2406.02049/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 51 references that are in the map.
- First posted on arXiv 4 Jun 2024.

## Builds on (most cited first, 15 of 15)

- [Avoiding Discrimination through Causal Reasoning](https://mlsanity.com/paper/1706.02744/) (2017)
- [An Introduction to Proximal Causal Learning](https://mlsanity.com/paper/2009.10982/) (2020)
- [Causal Machine Learning for Healthcare and Precision Medicine](https://mlsanity.com/paper/2205.11402/) (2022)
- [Identifiability of latent-variable and structural-equation models: from linear to nonlinear](https://mlsanity.com/paper/2302.02672/) (2023)
- [Learning Linear Non-Gaussian Causal Models in the Presence of Latent Variables](https://mlsanity.com/paper/1908.03932/) (2019)
- [Causal Discovery with Unobserved Confounding and non-Gaussian Data](https://mlsanity.com/paper/2007.11131/) (2020)
- [Causal Discovery with Latent Confounders Based on Higher-Order Cumulants](https://mlsanity.com/paper/2305.19582/) (2023)
- [A Cross-Moment Approach for Causal Effect Estimation](https://mlsanity.com/paper/2306.06263/) (2023)
- [Causal Discovery in Linear Latent Variable Models Subject to Measurement Error](https://mlsanity.com/paper/2211.03984/) (2022)
- [When does the ID algorithm fail?](https://mlsanity.com/paper/2307.03750/) (2023)
- [Causal inference in drug discovery and development](https://mlsanity.com/paper/2209.14664/) (2022)
- [Confidence Sets for Causal Orderings](https://mlsanity.com/paper/2305.14506/) (2023)
- [Parameter identification in linear non-Gaussian causal models under general confounding](https://mlsanity.com/paper/2405.20856/) (2024)
- [Sequential Underspecified Instrument Selection for Cause-Effect Estimation](https://mlsanity.com/paper/2302.05684/) (2023)
- [Proximal Causal Inference for Synthetic Control with Surrogates](https://mlsanity.com/paper/2308.09527/) (2023)

## Built on by (most cited first, 7 of 7)

- [Causal Additive Models with Unobserved Causal Paths and Backdoor Paths](https://mlsanity.com/paper/2502.07646/) (2025)
- [Causal Effect Identification in lvLiNGAM from Higher-Order Cumulants](https://mlsanity.com/paper/2506.05202/) (2025)
- [Causal Discovery in Linear Models with Unobserved Variables and Measurement Error](https://mlsanity.com/paper/2407.19426/) (2024)
- [Estimating Treatment Effects with Independent Component Analysis](https://mlsanity.com/paper/2507.16467/) (2025)
- [Trek-Based Parameter Identification for Linear Causal Models With Arbitrarily Structured Latent Variables](https://mlsanity.com/paper/2507.18170/) (2025)
- [Iterative Identification Closure: Amplifying Causal Identifiability in Linear SEMs](https://mlsanity.com/paper/2604.09309/) (2026)
- [Function-Valued Causal Influence in Nonlinear Time Series](https://mlsanity.com/paper/2605.26408/) (2026)

## Abstract (arXiv)

We study the generic identifiability of causal effects in linear non-Gaussian acyclic models (LiNGAM) with latent variables. We consider the problem in two main settings: When the causal graph is known a priori, and when it is unknown. In both settings, we provide a complete graphical characterization of the identifiable direct or total causal effects among observed variables. Moreover, we propose efficient algorithms to certify the graphical conditions. Finally, we propose an adaptation of the reconstruction independent component analysis (RICA) algorithm that estimates the causal effects from the observational data given the causal graph. Experimental results show the effectiveness of the proposed method in estimating the causal effects.