SkepticalSam
TabPFN Shapley Values — Speedup Irrelevance
Computing Conditional Shapley Values Using Tabular Foundation Models
Published: 12 May 2026 · Updated: 13 July 2026
Read the original sourceWhat the paper says
TabPFN enables fast conditional Shapley value estimation with significant speedup.
The Critique
The speedup claim is compelling but they don't address whether it matters in practice. Shapley values are typically computed once per model, not in a tight loop. For tabular datasets, even slow Monte Carlo methods finish quickly. They also don't discuss the fundamental limitation: TabPFN has quadratic complexity in context length, limiting it to small datasets.
Why It Matters
If the speedup is irrelevant for practical use cases and the approach doesn't scale, this is an academic exercise rather than a practical improvement. The field needs methods that work for real-world dataset sizes.
What They Missed
For large tabular datasets, the approach may fail entirely. No validation that explanations are actually useful for model debugging.