# Lifted Causal Inference in Relational Domains

arXiv: https://arxiv.org/abs/2403.10184 · HTML: https://mlsanity.com/paper/2403.10184/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 37 references that are in the map.
- First posted on arXiv 15 Mar 2024.

## Builds on (most cited first, 9 of 9)

- [Tractability through Exchangeability: A New Perspective on Efficient Probabilistic Inference](https://mlsanity.com/paper/1401.1247/) (2014)
- [Lifted Variable Elimination: Decoupling the Operators from the Constraint Language](https://mlsanity.com/paper/1402.0565/) (2014)
- [Causal Relational Learning](https://mlsanity.com/paper/2004.03644/) (2020)
- [A Sound and Complete Algorithm for Learning Causal Models from Relational Data](https://mlsanity.com/paper/1309.6843/) (2013)
- [Towards Robust Relational Causal Discovery](https://mlsanity.com/paper/1912.02390/) (2019)
- [Taming Reasoning in Temporal Probabilistic Relational Models](https://mlsanity.com/paper/1911.07040/) (2019)
- [Learning Relational Causal Models with Cycles through Relational Acyclification](https://mlsanity.com/paper/2208.12210/) (2022)
- [Lifted Representation of Relational Causal Models Revisited: Implications for Reasoning and Structure Learning](https://mlsanity.com/paper/1508.02103/) (2015)
- [Relational Causal Models with Cycles:Representation and Reasoning](https://mlsanity.com/paper/2202.10706/) (2022)

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

- [Estimating Causal Effects in Partially Directed Parametric Causal Factor Graphs](https://mlsanity.com/paper/2411.07006/) (2024)
- [Approximate Lifted Model Construction](https://mlsanity.com/paper/2504.20784/) (2025)
- [Lifting Factor Graphs with Some Unknown Factors](https://mlsanity.com/paper/2406.01275/) (2024)
- [Lifted Model Construction without Normalisation: A Vectorised Approach to Exploit Symmetries in Factor Graphs](https://mlsanity.com/paper/2411.11730/) (2024)
- [Towards Privacy-Preserving Relational Data Synthesis via Probabilistic Relational Models](https://mlsanity.com/paper/2409.04194/) (2024)
- [Lifting Factor Graphs with Some Unknown Factors for New Individuals](https://mlsanity.com/paper/2504.04089/) (2025)
- [Lifted Causal Inference](https://mlsanity.com/paper/2606.28024/) (2026)

## Abstract (arXiv)

Lifted inference exploits symmetries in probabilistic graphical models by using a representative for indistinguishable objects, thereby speeding up query answering while maintaining exact answers. Even though lifting is a well-established technique for the task of probabilistic inference in relational domains, it has not yet been applied to the task of causal inference. In this paper, we show how lifting can be applied to efficiently compute causal effects in relational domains. More specifically, we introduce parametric causal factor graphs as an extension of parametric factor graphs incorporating causal knowledge and give a formal semantics of interventions therein. We further present the lifted causal inference algorithm to compute causal effects on a lifted level, thereby drastically speeding up causal inference compared to propositional inference, e.g., in causal Bayesian networks. In our empirical evaluation, we demonstrate the effectiveness of our approach.