# Causality in Neural Networks -- An Extended Abstract

arXiv: https://arxiv.org/abs/2106.05842 · HTML: https://mlsanity.com/paper/2106.05842/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 18 references that are in the map.
- First posted on arXiv 3 Jun 2021.

## Builds on (most cited first, 10 of 10)

- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [Counterfactual Fairness](https://mlsanity.com/paper/1703.06856/) (2017)
- [Direct and Indirect Effects](https://mlsanity.com/paper/1301.2300/) (2013)
- [On the Transfer of Inductive Bias from Simulation to the Real World: a New Disentanglement Dataset](https://mlsanity.com/paper/1906.03292/) (2019)
- [Neural Network Attributions: A Causal Perspective](https://mlsanity.com/paper/1902.02302/) (2019)
- [Gender Slopes: Counterfactual Fairness for Computer Vision Models by Attribute Manipulation](https://mlsanity.com/paper/2005.10430/) (2020)
- [Learning Causal Models Online](https://mlsanity.com/paper/2006.07461/) (2020)
- [Supervising Feature Influence](https://mlsanity.com/paper/1803.10815/) (2018)

## Abstract (arXiv)

Causal reasoning is the main learning and explanation tool used by humans. AI systems should possess causal reasoning capabilities to be deployed in the real world with trust and reliability. Introducing the ideas of causality to machine learning helps in providing better learning and explainable models. Explainability, causal disentanglement are some important aspects of any machine learning model. Causal explanations are required to believe in a model's decision and causal disentanglement learning is important for transfer learning applications. We exploit the ideas of causality to be used in deep learning models to achieve better and causally explainable models that are useful in fairness, disentangled representation, etc.