# Causality-Based Feature Importance Quantifying Methods: PN-FI, PS-FI and PNS-FI

arXiv: https://arxiv.org/abs/2308.14474 · HTML: https://mlsanity.com/paper/2308.14474/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 27 references that are in the map.
- First posted on arXiv 28 Aug 2023.

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

- [Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations](https://mlsanity.com/paper/1811.12359/) (2018)
- [Causality for Machine Learning](https://mlsanity.com/paper/1911.10500/) (2019)
- [Learning Independent Causal Mechanisms](https://mlsanity.com/paper/1712.00961/) (2017)
- [Probabilities of Causation: Bounds and Identification](https://mlsanity.com/paper/1301.3898/) (2013)
- [Theoretical Impediments to Machine Learning With Seven Sparks from the Causal Revolution](https://mlsanity.com/paper/1801.04016/) (2018)
- [Causal de Finetti: On the Identification of Invariant Causal Structure in Exchangeable Data](https://mlsanity.com/paper/2203.15756/) (2022)
- [Personalized Decision Making -- A Conceptual Introduction](https://mlsanity.com/paper/2208.09558/) (2022)
- [Probabilities of Causation with Nonbinary Treatment and Effect](https://mlsanity.com/paper/2208.09568/) (2022)
- [Bounds on Causal Effects and Application to High Dimensional Data](https://mlsanity.com/paper/2106.12121/) (2021)
- [Unit Selection with Causal Diagram](https://mlsanity.com/paper/2109.07556/) (2021)
- [Epsilon-Identifiability of Causal Quantities](https://mlsanity.com/paper/2301.12022/) (2023)

## Abstract (arXiv)

In the current ML field models are getting larger and more complex, and data used for model training are also getting larger in quantity and higher in dimensions. Therefore, in order to train better models, and save training time and computational resources, a good Feature Selection (FS) method in the preprocessing stage is necessary. Feature importance (FI) is of great importance since it is the basis of feature selection. Therefore, this paper creatively introduces the calculation of PN (the probability of Necessity), PN (the probability of Sufficiency), and PNS (the probability of Necessity and Sufficiency) of Causality into quantifying feature importance and creates 3 new FI measuring methods, PN-FI, which means how much importance a feature has in image recognition tasks, PS-FI that means how much importance a feature has in image generating tasks, and PNS-FI which measures both. The main body of this paper is three RCTs, with whose results we show how PS-FI, PN-FI, and PNS-FI of 3 features, dog nose, dog eyes, and dog mouth are calculated. The experiments show that firstly, FI values are intervals with tight upper and lower bounds. Secondly, the feature dog eyes has the most importance while the other two have almost the same. Thirdly, the bounds of PNS and PN are tighter than the bounds of PS.