# Collaborative Causal Discovery with Atomic Interventions

arXiv: https://arxiv.org/abs/2106.03028 · HTML: https://mlsanity.com/paper/2106.03028/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 41 references that are in the map.
- First posted on arXiv 6 Jun 2021.

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

- [Causal Inference in the Presence of Latent Variables and Selection Bias](https://mlsanity.com/paper/1302.4983/) (2013)
- [Learning Causal Graphs with Small Interventions](https://mlsanity.com/paper/1511.00041/) (2015)
- [Budgeted Experiment Design for Causal Structure Learning](https://mlsanity.com/paper/1709.03625/) (2017)
- [Clustering with Same-Cluster Queries](https://mlsanity.com/paper/1606.02404/) (2016)
- [Learning and Testing Causal Models with Interventions](https://mlsanity.com/paper/1805.09697/) (2018)
- [Experimental Design for Cost-Aware Learning of Causal Graphs](https://mlsanity.com/paper/1810.11867/) (2018)
- [Causal Structure Discovery from Distributions Arising from Mixtures of DAGs](https://mlsanity.com/paper/2001.11940/) (2020)
- [Efficient Intervention Design for Causal Discovery with Latents](https://mlsanity.com/paper/2005.11736/) (2020)
- [Size of Interventional Markov Equivalence Classes in Random DAG Models](https://mlsanity.com/paper/1903.02054/) (2019)
- [Interventional Experiment Design for Causal Structure Learning](https://mlsanity.com/paper/1910.05651/) (2019)
- [Intervention Efficient Algorithms for Approximate Learning of Causal Graphs](https://mlsanity.com/paper/2012.13976/) (2020)

## Built on by (most cited first, 5 of 5)

- [Combinatorial Pure Exploration of Causal Bandits](https://mlsanity.com/paper/2206.07883/) (2022)
- [Deriving Causal Order from Single-Variable Interventions: Guarantees & Algorithm](https://mlsanity.com/paper/2405.18314/) (2024)
- [Identification of Average Causal Effects in Confounded Additive Noise Models](https://mlsanity.com/paper/2407.10014/) (2024)
- [Consistency evaluation of benchmarks used for causal discovery](https://mlsanity.com/paper/2606.01789/) (2026)
- [Causal Discovery Should Embrace the Wisdom of the Crowd](https://mlsanity.com/paper/2603.02678/) (2026)

## Abstract (arXiv)

We introduce a new Collaborative Causal Discovery problem, through which we model a common scenario in which we have multiple independent entities each with their own causal graph, and the goal is to simultaneously learn all these causal graphs. We study this problem without the causal sufficiency assumption, using Maximal Ancestral Graphs (MAG) to model the causal graphs, and assuming that we have the ability to actively perform independent single vertex (or atomic) interventions on the entities. If the M underlying (unknown) causal graphs of the entities satisfy a natural notion of clustering, we give algorithms that leverage this property and recovers all the causal graphs using roughly logarithmic in M number of atomic interventions per entity. These are significantly fewer than n atomic interventions per entity required to learn each causal graph separately, where n is the number of observable nodes in the causal graph. We complement our results with a lower bound and discuss various extensions of our collaborative setting.