# Learning Causal Models Online

arXiv: https://arxiv.org/abs/2006.07461 · HTML: https://mlsanity.com/paper/2006.07461/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jun 2020.

## Built on by (most cited first, 18 of 18)

- [Fishr: Invariant Gradient Variances for Out-of-Distribution Generalization](https://mlsanity.com/paper/2109.02934/) (2021)
- [MaskTune: Mitigating Spurious Correlations by Forcing to Explore](https://mlsanity.com/paper/2210.00055/) (2022)
- [A Theory of Label Propagation for Subpopulation Shift](https://mlsanity.com/paper/2102.11203/) (2021)
- [Provable Domain Generalization via Invariant-Feature Subspace Recovery](https://mlsanity.com/paper/2201.12919/) (2022)
- [Causal Incremental Graph Convolution for Recommender System Retraining](https://mlsanity.com/paper/2108.06889/) (2021)
- [Domain Generalization -- A Causal Perspective](https://mlsanity.com/paper/2209.15177/) (2022)
- [On Invariance Penalties for Risk Minimization](https://mlsanity.com/paper/2106.09777/) (2021)
- [FedGen: Generalizable Federated Learning for Sequential Data](https://mlsanity.com/paper/2211.01914/) (2022)
- [Continual Feature Selection: Spurious Features in Continual Learning](https://mlsanity.com/paper/2203.01012/) (2022)
- [Gated Information Bottleneck for Generalization in Sequential Environments](https://mlsanity.com/paper/2110.06057/) (2021)
- [Invariant-Feature Subspace Recovery: A New Class of Provable Domain Generalization Algorithms](https://mlsanity.com/paper/2311.00966/) (2023)
- [Balancing the Causal Effects in Class-Incremental Learning](https://mlsanity.com/paper/2402.10063/) (2024)
- [Causality in Neural Networks -- An Extended Abstract](https://mlsanity.com/paper/2106.05842/) (2021)
- [Individual and Collective Autonomous Development](https://mlsanity.com/paper/2109.11223/) (2021)
- [Diversity Boosted Learning for Domain Generalization with Large Number of Domains](https://mlsanity.com/paper/2207.13865/) (2022)
- [Continual Causal Abstractions](https://mlsanity.com/paper/2212.12575/) (2022)
- [Continual Invariant Risk Minimization](https://mlsanity.com/paper/2310.13977/) (2023)
- [Disentangled Representations for Causal Cognition](https://mlsanity.com/paper/2407.00744/) (2024)

## Abstract (arXiv)

Predictive models -- learned from observational data not covering the complete data distribution -- can rely on spurious correlations in the data for making predictions. These correlations make the models brittle and hinder generalization. One solution for achieving strong generalization is to incorporate causal structures in the models; such structures constrain learning by ignoring correlations that contradict them. However, learning these structures is a hard problem in itself. Moreover, it's not clear how to incorporate the machinery of causality with online continual learning. In this work, we take an indirect approach to discovering causal models. Instead of searching for the true causal model directly, we propose an online algorithm that continually detects and removes spurious features. Our algorithm works on the idea that the correlation of a spurious feature with a target is not constant over-time. As a result, the weight associated with that feature is constantly changing. We show that by continually removing such features, our method converges to solutions that have strong generalization. Moreover, our method combined with random search can also discover non-spurious features from raw sensory data. Finally, our work highlights that the information present in the temporal structure of the problem -- destroyed by shuffling the data -- is essential for detecting spurious features online.