mlsanityData to 14 Oct 2026

Comment on "Blessings of Multiple Causes"

arXiv 1910.05438 ↗First posted 11 Oct 2019stat.ME stat.MLcited by 16

Not on the map. Why?

Abstract (arXiv)

(This comment has been updated to respond to Wang and Blei's rejoinder [arXiv:1910.07320].) The premise of the deconfounder method proposed in "Blessings of Multiple Causes" by Wang and Blei [arXiv:1805.06826], namely that a variable that renders multiple causes conditionally independent also controls for unmeasured multi-cause confounding, is incorrect. This can be seen by noting that no fact about the observed data alone can be informative about ignorability, since ignorability is compatible with any observed data distribution. Methods to control for unmeasured confounding may be valid with additional assumptions in specific settings, but they cannot, in general, provide a checkable approach to causal inference, and they do not, in general, require weaker assumptions than the assumptions that are commonly used for causal inference. While this is outside the scope of this comment, we note that much recent work on applying ideas from latent variable modeling to causal inference problems suffers from similar issues.

Key facts. Cited by 16 papers in the map (data through 14 Oct 2026). First posted on arXiv 11 Oct 2019.

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Citing papers per year: 2021: 5; 2022: 2; 2023: 3; 2024: 4; 2025: 0; 2026: 2.

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