# FASK with Interventional Knowledge Recovers Edges from the Sachs Model

arXiv: https://arxiv.org/abs/1805.03108 · HTML: https://mlsanity.com/paper/1805.03108/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 May 2018.

## Built on by (most cited first, 14 of 14)

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- [The interventional Bayesian Gaussian equivalent score for Bayesian causal inference with unknown soft interventions](https://mlsanity.com/paper/2205.02602/) (2022)
- [Causal Dependence Plots](https://mlsanity.com/paper/2303.04209/) (2023)
- [REX: Causal discovery based on machine learning and explainability techniques](https://mlsanity.com/paper/2501.12706/) (2025)
- [Fast Nonparametric Conditional Independence Testing via Two-Stage Regression](https://mlsanity.com/paper/2606.18011/) (2026)
- [Can We Utilize Pre-trained Language Models within Causal Discovery Algorithms?](https://mlsanity.com/paper/2311.11212/) (2023)
- [Differentiable Causal Discovery for Singular Linear Models under Confounding](https://mlsanity.com/paper/2601.01368/) (2026)
- [Causal Discovery Under Hard Selection Bias: A New Robust Score-Matching Approach](https://mlsanity.com/paper/2610.18075/) (2026)

## Abstract (arXiv)

We report a procedure that, in one step from continuous data with minimal preparation, recovers the graph found by Sachs et al. citesachs2005causal, with only a few edges different. The algorithm, Fast Adjacency Skewness (FASK), relies on a mixture of linear reasoning and reasoning from the skewness of variables; the Sachs data is a good candidate for this procedure since the skewness of the variables is quite pronounced. We review the ground truth model from Sachs et al. as well as some of the fluctuations seen in the protein abundances in the system, give the Sachs model and the FASK model, and perform a detailed comparison. Some variation in hyper-parameters is explored, though the main result uses values at or near the defaults learned from work modeling fMRI data.