Position: Causal Machine Learning Requires Rigorous Synthetic Experiments for Broader Adoption
This addresses the problem of underutilization of causal machine learning by the broader ML community due to inadequate evaluation practices, though it is incremental in proposing guidelines rather than new methods.
The paper argues that synthetic experiments are essential for evaluating causal machine learning methods, as current practices fail to assess reliability and robustness, and proposes principles for rigorous synthetic analyses to build trust and drive adoption.
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 evaluations do not permit assessment of their reliability and robustness, undermining their practical utility. Specifically, one of the principal criticisms made by the community is the extensive use of synthetic experiments. We argue, on the contrary, that synthetic experiments are essential and necessary to precisely assess and understand the capabilities of causal machine learning methods. To substantiate our position, we critically review the current evaluation practices, spotlight their shortcomings, and propose a set of principles for conducting rigorous empirical analyses with synthetic data. Adopting the proposed principles will enable comprehensive evaluations that build trust in causal machine learning methods, driving their broader adoption and impactful real-world use.