# Causal Discovery with a Mixture of DAGs

arXiv: https://arxiv.org/abs/1901.09475 · HTML: https://mlsanity.com/paper/1901.09475/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jan 2019.

## Built on by (most cited first, 7 of 7)

- [Causal Structure Learning: a Combinatorial Perspective](https://mlsanity.com/paper/2206.01152/) (2022)
- [Discovering Mixtures of Structural Causal Models from Time Series Data](https://mlsanity.com/paper/2310.06312/) (2023)
- [Disentangling Mixtures of Unknown Causal Interventions](https://mlsanity.com/paper/2210.03242/) (2022)
- [Tackling Non-Stationarity in Reinforcement Learning via Causal-Origin Representation](https://mlsanity.com/paper/2306.02747/) (2023)
- [Learning Multiscale Non-stationary Causal Structures](https://mlsanity.com/paper/2208.14989/) (2022)
- [NOTMAD: Estimating Bayesian Networks with Sample-Specific Structures and Parameters](https://mlsanity.com/paper/2111.01104/) (2021)
- [Generalizing Clinical Trials with Convex Hulls](https://mlsanity.com/paper/2111.13229/) (2021)

## Abstract (arXiv)

Causal processes in biomedicine may contain cycles, evolve over time or differ between populations. However, many graphical models cannot accommodate these conditions. We propose to model causation using a mixture of directed cyclic graphs (DAGs), where the joint distribution in a population follows a DAG at any single point in time but potentially different DAGs across time. We also introduce an algorithm called Causal Inference over Mixtures that uses longitudinal data to infer a graph summarizing the causal relations generated from a mixture of DAGs. Experiments demonstrate improved performance compared to prior approaches.