# Meta Learning for Causal Direction

arXiv: https://arxiv.org/abs/2007.02809 · HTML: https://mlsanity.com/paper/2007.02809/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jul 2020.

## Built on by (most cited first, 16 of 16)

- [Causal Discovery from Temporal Data: An Overview and New Perspectives](https://mlsanity.com/paper/2303.10112/) (2023)
- [MECD: Unlocking Multi-Event Causal Discovery in Video Reasoning](https://mlsanity.com/paper/2409.17647/) (2024)
- [A Meta-Reinforcement Learning Algorithm for Causal Discovery](https://mlsanity.com/paper/2207.08457/) (2022)
- [Hacking Task Confounder in Meta-Learning](https://mlsanity.com/paper/2312.05771/) (2023)
- [CORE: Towards Scalable and Efficient Causal Discovery with Reinforcement Learning](https://mlsanity.com/paper/2401.16974/) (2024)
- [MECD+: Unlocking Event-Level Causal Graph Discovery for Video Reasoning](https://mlsanity.com/paper/2501.07227/) (2025)
- [Learning to Embed Distributions via Maximum Kernel Entropy](https://mlsanity.com/paper/2408.00549/) (2024)
- [Learning to Infer Counterfactuals: Meta-Learning for Estimating Multiple Imbalanced Treatment Effects](https://mlsanity.com/paper/2208.06748/) (2022)
- [Meta-learning for heterogeneous treatment effect estimation with closed-form solvers](https://mlsanity.com/paper/2305.11353/) (2023)
- [Test-Time Learning of Causal Structure from Interventional Data](https://mlsanity.com/paper/2602.19131/) (2026)
- [A Meta Learning Approach to Discerning Causal Graph Structure](https://mlsanity.com/paper/2106.05859/) (2021)
- [Causal Time Series Modeling of Supraglacial Lake Evolution in Greenland under Distribution Shift](https://mlsanity.com/paper/2510.15265/) (2025)
- [Consistency evaluation of benchmarks used for causal discovery](https://mlsanity.com/paper/2606.01789/) (2026)
- [Uplift Modeling based on Graph Neural Network Combined with Causal Knowledge](https://mlsanity.com/paper/2311.08434/) (2023)
- [Causal Transfer in Medical Image Analysis](https://mlsanity.com/paper/2603.24388/) (2026)
- [Inferring Causal Relations between Two Sequences of Events with Language Models](https://mlsanity.com/paper/2609.39406/) (2026)

## Abstract (arXiv)

The inaccessibility of controlled randomized trials due to inherent constraints in many fields of science has been a fundamental issue in causal inference. In this paper, we focus on distinguishing the cause from effect in the bivariate setting under limited observational data. Based on recent developments in meta learning as well as in causal inference, we introduce a novel generative model that allows distinguishing cause and effect in the small data setting. Using a learnt task variable that contains distributional information of each dataset, we propose an end-to-end algorithm that makes use of similar training datasets at test time. We demonstrate our method on various synthetic as well as real-world data and show that it is able to maintain high accuracy in detecting directions across varying dataset sizes.