Research·arXiv cs.LG·May 8

Bayesian Sensitivity of Causal Inference Estimators under Evidence-Based Priors

Causal inference in machine learning depends on untestable assumptions about data generation, creating a persistent vulnerability in observational studies. This work challenges the field's reliance on worst-case sensitivity analysis, arguing that pessimistic bounds often become uninformative or contradict domain knowledge. By extending the s-value framework to three core causal assumptions, the authors demonstrate that realistic priors can yield more actionable robustness guarantees. The shift from adversarial to evidence-based sensitivity testing matters for practitioners deploying ML in high-stakes domains like healthcare and policy, where false confidence in causal estimates can propagate downstream.

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Explainer

The paper's core move is reframing sensitivity analysis from a worst-case defense (bounds that hold for any possible violation of assumptions) to a prior-informed one (bounds that hold given realistic domain knowledge). This is not just a tweak to existing methods; it's a philosophical inversion about what 'robustness' should mean when practitioners have legitimate prior information.

This directly extends the concern raised in the mechanistic interpretability audit from May 8th, which flagged that causal claims routinely lack explicit identification assumptions. Where that paper called for disclosure norms, Gupta and Rothenhäusler provide a concrete tool: the s-value framework lets practitioners quantify how sensitive their causal estimates are to assumption violations, given what they actually know about their domain. The two papers form a tighter loop: first, state your assumptions; second, test robustness against realistic violations of those assumptions rather than paranoid ones.

If this framework gets adopted in healthcare ML deployments over the next 18 months (watch for citations in FDA submissions or clinical ML papers), it signals that practitioners are moving away from worst-case bounds that often paralyze decision-making. If it remains confined to academic papers, the gap between what robustness theory recommends and what practitioners actually use will have widened further.

Coverage we drew on

Position: Mechanistic Interpretability Must Disclose Identification Assumptions for Causal Claims · arXiv cs.LG

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MentionsGupta · Rothenhäusler

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