# Identifying Causal Effects with the R Package causaleffect

arXiv: https://arxiv.org/abs/1806.07161 · HTML: https://mlsanity.com/paper/1806.07161/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jun 2018.

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

- [D'ya like DAGs? A Survey on Structure Learning and Causal Discovery](https://mlsanity.com/paper/2103.02582/) (2021)
- [Causal Effect Identification in Cluster DAGs](https://mlsanity.com/paper/2202.12263/) (2022)
- [DoWhy: Addressing Challenges in Expressing and Validating Causal Assumptions](https://mlsanity.com/paper/2108.13518/) (2021)
- [Clustering and Structural Robustness in Causal Diagrams](https://mlsanity.com/paper/2111.04513/) (2021)
- [DeCaFlow: A deconfounding causal generative model](https://mlsanity.com/paper/2503.15114/) (2025)
- [Practical do-Shapley Explanations with Estimand-Agnostic Causal Inference](https://mlsanity.com/paper/2509.20211/) (2025)
- [Investigating potential causes of Sepsis with Bayesian network structure learning](https://mlsanity.com/paper/2406.09207/) (2024)
- [Leveraging Structured Biological Knowledge for Counterfactual Inference: a Case Study of Viral Pathogenesis](https://mlsanity.com/paper/2101.05136/) (2021)
- [Algorithmic Causal Effect Identification with causaleffect](https://mlsanity.com/paper/2107.04632/) (2021)
- [Identifiability of Causal-based Fairness Notions: A State of the Art](https://mlsanity.com/paper/2203.05900/) (2022)
- [Econometric vs. Causal Structure-Learning for Time-Series Policy Decisions: Evidence from the UK COVID-19 Policies](https://mlsanity.com/paper/2603.00041/) (2026)
- [Generalizing experimental findings: identification beyond adjustments](https://mlsanity.com/paper/2206.06699/) (2022)
- [An R package for parametric estimation of causal effects](https://mlsanity.com/paper/2307.08686/) (2023)
- [Optimal Observation-Intervention Trade-Off in Optimisation Problems with Causal Structure](https://mlsanity.com/paper/2309.02287/) (2023)
- [Robot Pouring: Identifying Causes of Spillage and Selecting Alternative Action Parameters Using Probabilistic Actual Causation](https://mlsanity.com/paper/2502.09395/) (2025)
- [A Computational Theory for Efficient Mini Agent Evaluation with Causal Guarantees](https://mlsanity.com/paper/2503.21138/) (2025)
- [Causal identification with Y0](https://mlsanity.com/paper/2508.03167/) (2025)
- [Scaling Causal Mediation for Complex Systems: A Framework for Root Cause Analysis](https://mlsanity.com/paper/2512.14764/) (2025)
- [Reasoning Models Are Accurate but Unsound on Identification](https://mlsanity.com/paper/2610.03519/) (2026)

## Abstract (arXiv)

Do-calculus is concerned with estimating the interventional distribution of an action from the observed joint probability distribution of the variables in a given causal structure. All identifiable causal effects can be derived using the rules of do-calculus, but the rules themselves do not give any direct indication whether the effect in question is identifiable or not. Shpitser and Pearl constructed an algorithm for identifying joint interventional distributions in causal models, which contain unobserved variables and induce directed acyclic graphs. This algorithm can be seen as a repeated application of the rules of do-calculus and known properties of probabilities, and it ultimately either derives an expression for the causal distribution, or fails to identify the effect, in which case the effect is non-identifiable. In this paper, the R package causaleffect is presented, which provides an implementation of this algorithm. Functionality of causaleffect is also demonstrated through examples.