Using Propensity Scores for Causal Inference in Product Experimentation
product experimentation•Apr 30, 2026

Using Propensity Scores for Causal Inference in Product Experimentation

Product experimentation teams often struggle with causal inference on LLM-based features due to the opt-in trap, where users who click to opt in differ systematically from those who don't. Propensity score methods can help reweight comparison groups to approximate a randomized experiment.

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Key summary

This article discusses the challenges of product experimentation with LLM-based features and how propensity scores can be used for causal inference to separate adoption bias from the feature's actual effect.

TL;DR

  • The opt-in trap is a common issue in product experimentation with LLM-based features.
  • Propensity scores can be used for causal inference to separate adoption bias from the feature's actual effect.
  • The process involves propensity estimation, inverse-probability weighting, nearest-neighbor matching, balance diagnostics, and bootstrap confidence intervals.
  • A tutorial is provided on a 50,000-user synthetic SaaS dataset to estimate the causal effect and quantify uncertainty.
  • Companion code is available on GitHub for running the pipeline end-to-end.
#product experimentation
#causal inference
#ai
#machine learning
#experimentation

This content is automatically summarized using AI. For full context, always refer to the original article.

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