# Supervised Whole DAG Causal Discovery

arXiv: https://arxiv.org/abs/2006.04697 · HTML: https://mlsanity.com/paper/2006.04697/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jun 2020.

## Built on by (most cited first, 19 of 19)

- [Amortized Inference for Causal Structure Learning](https://mlsanity.com/paper/2205.12934/) (2022)
- [Learning to Induce Causal Structure](https://mlsanity.com/paper/2204.04875/) (2022)
- [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-Learning Approach to Bayesian Causal Discovery](https://mlsanity.com/paper/2412.16577/) (2024)
- [ML4C: Seeing Causality Through Latent Vicinity](https://mlsanity.com/paper/2110.00637/) (2021)
- [Demystifying amortized causal discovery with transformers](https://mlsanity.com/paper/2405.16924/) (2024)
- [Causal discovery for observational sciences using supervised machine learning](https://mlsanity.com/paper/2202.12813/) (2022)
- [Embracing the black box: Heading towards foundation models for causal discovery from time series data](https://mlsanity.com/paper/2402.09305/) (2024)
- [Are you doing better than random guessing? A call for using negative controls when evaluating causal discovery algorithms](https://mlsanity.com/paper/2412.10039/) (2024)
- [MECD+: Unlocking Event-Level Causal Graph Discovery for Video Reasoning](https://mlsanity.com/paper/2501.07227/) (2025)
- [Learning Causal Graphs at Scale: A Foundation Model Approach](https://mlsanity.com/paper/2506.18285/) (2025)
- [Learned Causal Method Prediction](https://mlsanity.com/paper/2311.03989/) (2023)
- [Test-Time Learning of Causal Structure from Interventional Data](https://mlsanity.com/paper/2602.19131/) (2026)
- [Identifying biological perturbation targets through causal differential networks](https://mlsanity.com/paper/2410.03380/) (2024)
- [ACTIVA: Amortized Causal Effect Estimation via Transformer-based Variational Autoencoder](https://mlsanity.com/paper/2503.01290/) (2025)
- [Graph Structure Inference with BAM: Introducing the Bilinear Attention Mechanism](https://mlsanity.com/paper/2402.07735/) (2024)
- [From Observations to Causations: A GNN-based Probabilistic Prediction Framework for Causal Discovery](https://mlsanity.com/paper/2507.20349/) (2025)
- [Demistifying Data and Simulator Assumptions in Supervised Causal Discovery](https://mlsanity.com/paper/2609.37446/) (2026)

## Abstract (arXiv)

We propose to address the task of causal structure learning from data in a supervised manner. Existing work on learning causal directions by supervised learning is restricted to learning pairwise relation, and not well suited for whole DAG discovery. We propose a novel approach of modeling the whole DAG structure discovery as a supervised learning. To fit the problem in hand, we propose to use permutation equivariant models that align well with the problem domain. We evaluate the proposed approach extensively on synthetic graphs of size 10,20,50,100 and real data, and show promising results compared with a variety of previous approaches.