Why Even Well-Executed A/B Tests Can Fail to Answer the Real Business Question

Summary

The article discusses how experiments (particularly A/B tests) often fail not because of technical execution, but because the experimental design cannot actually answer the business question being asked. It introduces the concept of "identifiability" — whether an experiment's design can truly isolate and measure the causal effect of interest. The author argues that as AI makes running experiments easier, the critical question shifts from "Can I run this test?" to "Should I run this test?" — emphasizing that system constraints, decision environments, and design flaws can render even statistically powered experiments incapable of answering the real business question.

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