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Panayiotis Panayiotou

2 accepted papers

2026

CausalProfiler: Generating Synthetic Benchmarks for Rigorous and Transparent Evaluation of Causal Machine Learning

ICML 2026poster

Causal machine learning (Causal ML) aims to answer "what if" questions using machine learning algorithms, making it a promising tool for high-stakes decision-making. Yet, empirical evaluation practices in Causal ML remain limited. Existing benchmarks often rely on a handful of hand-crafted or semi-s…

Cited by 0SourceScholar
2025

Position: Causal Machine Learning Requires Rigorous Synthetic Experiments for Broader Adoption

ICML 2025poster

Causal machine learning has the potential to revolutionize decision-making by combining the predictive power of machine learning algorithms with the theory of causal inference. However, these methods remain underutilized by the broader machine learning community, in part because current empirical ev…

Cited by 0SourcePDFScholar