Instrumental Variable Analysis Without Structural Equations

Instrumental Variable Analysis Without Structural Equations

🎙 Dr. Aurélien Bibaut 👥 8K 📅 June 17, 2026 ⏱ 76 min 👁 312 📄 original study 🧭 2026-08-15
Available in: English (current) Français

Keywords

instrumental variablenonparametricsurrogate indexcausal inferenceidentification

Summary

Dr. Aurélien Bibaut presents a framework for instrumental variable (IV) analysis without relying on structural equations, motivated by applications at Netflix such as evaluating the effect of watching a live event on retention. He introduces the concept of a projection parameter, which is a linear functional of the structural function, and discusses its identification under the NPIV model. The talk covers the challenges of over-identification and under-identification, and proposes a method based on Riesz representers and conditional moment restrictions. He illustrates the approach with examples from weak experiments and deconfounded surrogates, and discusses inference procedures. The presentation is technical, aimed at an audience familiar with causal inference and statistical learning.

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Critical Evaluation

Value of the Information & Strength of the Argument

The talk provides valuable insights into a novel approach to IV analysis, addressing limitations of traditional structural equation models. The argumentation is rigorous, building from motivation to formal definitions and identification conditions. The speaker clearly explains the intuition behind the projection parameter and its relevance in practical settings like A/B testing and surrogate endpoints. The discussion of over- and under-identification is particularly insightful, linking to classical econometric concepts. The presentation is well-structured and logically coherent.

Scientific Rigor, Source Quality, Title Accuracy

The talk is based on several papers, including ‘NPIV with many weak instruments’ and ‘Non-parametric IV analysis without structural equations’, which are cited in the description. The speaker references these works and mentions collaborators, indicating a solid academic foundation. The title accurately reflects the content, focusing on IV analysis without structural equations. The presentation is scientifically rigorous, with clear mathematical definitions and assumptions. The description provides links to the INI seminar page and the institute’s website, which serve as sources for further information.

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Title / Content Match

The title accurately reflects the content, which focuses on instrumental variable analysis without relying on structural equations, proposing a projection parameter approach.

Quality & Reliability

8/10

The talk presents original research from a published paper, with rigorous mathematical formalism and clear explanations. The speaker is a researcher at Netflix and the work is part of an INI workshop on causality and machine learning, indicating academic credibility.

Key Moments

Cited Sources

Concurring Sources

  • Non-parametric IV analysis without structural equations — Paper by Bibaut et al. that forms the basis of the talk.

Contribution & Novelties

The talk presents a novel approach to IV analysis that avoids structural equations, focusing on a projection parameter. This allows for more flexible modeling and addresses issues of over-identification. The method is applied to deconfounded surrogates, which is a practical contribution for tech companies. The presentation also discusses inference procedures and weak instruments.

Pour aller plus loin :

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Radar Profile

The radar profile shows high scores in information quality and technical level, with slightly lower but still strong scores in quantity and reliability. This indicates a technically dense and reliable presentation, though it may be less accessible to a general audience.

Reliability 8/10

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