# Integrating overlapping datasets using bivariate causal discovery

arXiv: https://arxiv.org/abs/1910.11356 · HTML: https://mlsanity.com/paper/1910.11356/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Oct 2019.

## Built on by (most cited first, 12 of 12)

- [Estimating long-term causal effects from short-term experiments and long-term observational data with unobserved confounding](https://mlsanity.com/paper/2302.10625/) (2023)
- [Disentangling Causal Effects from Sets of Interventions in the Presence of Unobserved Confounders](https://mlsanity.com/paper/2210.05446/) (2022)
- [Non-parametric identifiability and sensitivity analysis of synthetic control models](https://mlsanity.com/paper/2301.07656/) (2023)
- [Spillover Detection for Donor Selection in Synthetic Control Models](https://mlsanity.com/paper/2406.11399/) (2024)
- [Cross-validating causal discovery via Leave-One-Variable-Out](https://mlsanity.com/paper/2411.05625/) (2024)
- [SSL Framework for Causal Inconsistency between Structures and Representations](https://mlsanity.com/paper/2310.18634/) (2023)
- [Estimating Joint Interventional Distributions from Marginal Interventional Data](https://mlsanity.com/paper/2409.01794/) (2024)
- [Constraint-based causal discovery with tiered background knowledge and latent variables in single or overlapping datasets](https://mlsanity.com/paper/2503.21526/) (2025)
- [Mediated Uncoupled Learning: Learning Functions without Direct Input-output Correspondences](https://mlsanity.com/paper/2107.08135/) (2021)
- [Towards Causal Representation Learning and Deconfounding from Indefinite Data](https://mlsanity.com/paper/2305.02640/) (2023)
- [Towards Causal Relationship in Indefinite Data: Baseline Model and New Datasets](https://mlsanity.com/paper/2401.08221/) (2024)
- [Jigsaw-CRL: Recovering Global Latent Causal Order from Fragmented Multi-Client Interventions](https://mlsanity.com/paper/2608.28991/) (2026)

## Abstract (arXiv)

Causal knowledge is vital for effective reasoning in science, as causal relations, unlike correlations, allow one to reason about the outcomes of interventions. Algorithms that can discover causal relations from observational data are based on the assumption that all variables have been jointly measured in a single dataset. In many cases this assumption fails. Previous approaches to overcoming this shortcoming devised algorithms that returned all joint causal structures consistent with the conditional independence information contained in each individual dataset. But, as conditional independence tests only determine causal structure up to Markov equivalence, the number of consistent joint structures returned by these approaches can be quite large. The last decade has seen the development of elegant algorithms for discovering causal relations beyond conditional independence, which can distinguish among Markov equivalent structures. In this work we adapt and extend these so-called bivariate causal discovery algorithms to the problem of learning consistent causal structures from multiple datasets with overlapping variables belonging to the same generating process, providing a sound and complete algorithm that outperforms previous approaches on synthetic and real data.