# Causal Discovery Under Local Privacy

arXiv: https://arxiv.org/abs/2311.04037 · HTML: https://mlsanity.com/paper/2311.04037/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 61 references that are in the map.
- First posted on arXiv 7 Nov 2023.

## Builds on (most cited first, 6 of 6)

- [Causal Reasoning for Algorithmic Fairness](https://mlsanity.com/paper/1805.05859/) (2018)
- [Causal Inference with Corrupted Data: Measurement Error, Missing Values, Discretization, and Differential Privacy](https://mlsanity.com/paper/2107.02780/) (2021)
- [Causal Discovery for Fairness](https://mlsanity.com/paper/2206.06685/) (2022)
- [Locally Private Causal Inference for Randomized Experiments](https://mlsanity.com/paper/2301.01616/) (2023)
- [Castell: Scalable Joint Probability Estimation of Multi-dimensional Data Randomized with Local Differential Privacy](https://mlsanity.com/paper/2212.01627/) (2022)
- [Galton's Family Heights Data Revisited](https://mlsanity.com/paper/1508.02942/) (2015)

## Built on by (most cited first, 3 of 3)

- [Causality Is Key to Understand and Balance Multiple Goals in Trustworthy ML and Foundation Models](https://mlsanity.com/paper/2502.21123/) (2025)
- [Trustworthy AI Suffers from Invariance Conflicts and Causality is The Solution](https://mlsanity.com/paper/2605.02640/) (2026)
- [Towards Privacy-Aware Bayesian Networks: A Credal Approach](https://mlsanity.com/paper/2509.18949/) (2025)

## Abstract (arXiv)

Differential privacy is a widely adopted framework designed to safeguard the sensitive information of data providers within a data set. It is based on the application of controlled noise at the interface between the server that stores and processes the data, and the data consumers. Local differential privacy is a variant that allows data providers to apply the privatization mechanism themselves on their data individually. Therefore it provides protection also in contexts in which the server, or even the data collector, cannot be trusted. The introduction of noise, however, inevitably affects the utility of the data, particularly by distorting the correlations between individual data components. This distortion can prove detrimental to tasks such as causal discovery. In this paper, we consider various well-known locally differentially private mechanisms and compare the trade-off between the privacy they provide, and the accuracy of the causal structure produced by algorithms for causal learning when applied to data obfuscated by these mechanisms. Our analysis yields valuable insights for selecting appropriate local differentially private protocols for causal discovery tasks. We foresee that our findings will aid researchers and practitioners in conducting locally private causal discovery.