How much do we really know about Structure Learning from i.i.d. Data? Interpretable, multi-dimensional Performance Indicator for Causal Discovery

• URL: http://arxiv.org/abs/2409.19377v1
• Date: Sat, 28 Sep 2024 15:03:49 GMT
• Title: How much do we really know about Structure Learning from i.i.d. Data? Interpretable, multi-dimensional Performance Indicator for Causal Discovery
• Authors: Georg Velev, Stefan Lessmann,
• Abstract summary: causal discovery from observational data imposes strict identifiability assumptions on the formulation of structural equations utilized in the data generating process. Motivated by the lack of unified performance assessment framework, we introduce an interpretable, six-dimensional evaluation metric, i.e., distance to optimal solution (DOS) This is the first research to assess the performance of structure learning algorithms from seven different families on increasing percentage of non-identifiable, nonlinear causal patterns.
• Score: 3.8443430569753025
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Nonlinear causal discovery from observational data imposes strict identifiability assumptions on the formulation of structural equations utilized in the data generating process. The evaluation of structure learning methods under assumption violations requires a rigorous and interpretable approach, which quantifies both the structural similarity of the estimation with the ground truth and the capacity of the discovered graphs to be used for causal inference. Motivated by the lack of unified performance assessment framework, we introduce an interpretable, six-dimensional evaluation metric, i.e., distance to optimal solution (DOS), which is specifically tailored to the field of causal discovery. Furthermore, this is the first research to assess the performance of structure learning algorithms from seven different families on increasing percentage of non-identifiable, nonlinear causal patterns, inspired by real-world processes. Our large-scale simulation study, which incorporates seven experimental factors, shows that besides causal order-based methods, amortized causal discovery delivers results with comparatively high proximity to the optimal solution. In addition to the findings from our sensitivity analysis, we explore interactions effects between the experimental factors of our simulation framework in order to provide transparency about the expected performance of causal discovery techniques in different scenarios.

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