CausalityCheck
A no-code analytics add-on that flags when an A/B test, growth experiment, or observational dataset has hidden confounders strong enough to flip a causal conclusion.
Growth analysts and product managers running experiments at SaaS companies
- One-click scan of an experiment result and a plain-English verdict: Safe to claim cause / Borderline / Do not claim cause
- Sensitivity grid showing what confounder strength would be needed to flip the conclusion
- Sanity-check templates for common shapes like pricing tests, onboarding tweaks, and marketing attribution
- Shareable audit card so analysts can hand a defensible summary to Legal, PR, or the CMO
The arXiv finding shows more data actually lowers the threshold at which Bayesian causal discovery silently picks spurious edges, meaning the standard 'p-value dropped, ship it' workflow at growth teams is quietly wrong more often than people realise.
Large A/B testing market (~$1.4B in 2024, 11-15% CAGR) confirms growth-experiment spend is real, but I found no direct evidence of demand for a confounder-detection add-on specifically; the motivating arXiv paper did not return in search, weakening the 'why now' hook.A/B Testing Software Market Size, Share and Growth Report 2032 ↗A/B Testing Platform Market Research Report 2033 ↗
No direct competitor found doing no-code confounder flagging as an add-on; Statsig publishes on causal inference as content rather than a product, and incumbents (Eppo, Optimizely) focus on full experimentation stacks, leaving room — but feature-parity risk is real.Causal inference in product experimentation - statsig.com ↗
No pricing/WTP data for this specific add-on; growth teams pay for platforms (Statsig, Eppo, LaunchDarkly) but a niche add-on SKU targeting confounders is unproven, and the 'ship it' workflow pain rarely surfaces in tool-budget discussions.Best A/B Testing Tools for Beginners (2026) ↗
Confounding in experiments is an evergreen methodological problem reinforced by the causal-inference trend (Stanford Causal AI Lab, multiple Cambridge methods papers), so demand drivers are structural rather than hype-driven.Stanford Causal AI Lab ↗Methods in causal inference. Part 4: confounding in experiments ↗
Doable by wrapping existing causal-discovery libraries (DoWhy, CausalImpact) behind a no-code UI and a thin connector to experimentation platforms; the main friction is per-platform integrations and explaining results to non-statistician PMs.Confounders - Causal Inference Studies ↗