$\texttt{causalAssembly}$: Generating Realistic Production Data for Benchmarking Causal Discovery

TL;DR

This paper introduces causalAssembly, a Python library that generates realistic semi-synthetic manufacturing data with known causal relationships, enabling benchmarking of causal discovery algorithms in complex, real-world scenarios.

Contribution

The paper presents a novel system for creating semi-synthetic data with ground truth causal relations, facilitating empirical validation of causal discovery methods.

Findings

Effective benchmarking of causal discovery algorithms demonstrated

Distributional random forests enable flexible conditional distribution estimation

Ground truth causal relationships improve validation accuracy

Abstract

Algorithms for causal discovery have recently undergone rapid advances and increasingly draw on flexible nonparametric methods to process complex data. With these advances comes a need for adequate empirical validation of the causal relationships learned by different algorithms. However, for most real data sources true causal relations remain unknown. This issue is further compounded by privacy concerns surrounding the release of suitable high-quality data. To help address these challenges, we gather a complex dataset comprising measurements from an assembly line in a manufacturing context. This line consists of numerous physical processes for which we are able to provide ground truth causal relationships on the basis of a detailed study of the underlying physics. We use the assembly line data and associated ground truth information to build a system for generation of semisynthetic manufacturing data that supports benchmarking of causal discovery methods. To accomplish this, we employ distributional random forests in order to flexibly estimate and represent conditional distributions that may be combined into joint distributions that strictly adhere to a causal model over the observed variables. The estimated conditionals and tools for data generation are made available in our Python library $\texttt{causalAssembly}$. Using the library, we showcase how to benchmark several well-known causal discovery algorithms.