Self-consistency is a weak, regime-dependent proxy for correctness: positive but small correlations (rho 0.20–0.59), with the most self-consistent frontier model over-confident and wrong 48% of the time at high agreement.
Iterative Causal Segmentation: Filling the Gap between Market Segmentation and Marketing Strategy
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abstract
The field of causal Machine Learning (ML) has made significant strides in recent years. Notable breakthroughs include methods such as meta learners (arXiv:1706.03461v6) and heterogeneous doubly robust estimators (arXiv:2004.14497) introduced in the last five years. Despite these advancements, the field still faces challenges, particularly in managing tightly coupled systems where both the causal treatment variable and a confounding covariate must serve as key decision-making indicators. This scenario is common in applications of causal ML for marketing, such as marketing segmentation and incremental marketing uplift. In this work, we present our formally proven algorithm, iterative causal segmentation, to address this issue.
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When LLMs Agree, Are They Right? Auditing Self-Consistency and Cross-Model Agreement as Confidence Signals
Self-consistency is a weak, regime-dependent proxy for correctness: positive but small correlations (rho 0.20–0.59), with the most self-consistent frontier model over-confident and wrong 48% of the time at high agreement.