Haus

Haus is a leading causal inference platform designed for modern marketing organizations to measure the true incremental impact of their advertising spend. In the 2026 landscape, where deterministic tracking (cookies) has effectively been phased out, Haus provides a robust technical alternative by utilizing randomized experiments and geographic-based testing frameworks. Its architecture leverages advanced Bayesian models to account for seasonality, baseline trends, and external noise, ensuring that marketers can differentiate between organic growth and paid-media-driven revenue. The platform specializes in solving the 'attribution problem' by moving away from flawed last-click models toward a methodology rooted in experimental design (RCTs). Haus’s 2026 positioning focuses on being the source of truth for high-growth brands that require precise ROI data across complex, multi-channel environments including Social, Search, CTV, and Out-of-Home. By automating the market-selection process for geo-tests and providing real-time causal lift analysis, Haus enables data science teams and CMOs to confidently reallocate millions in budget toward the most efficient growth levers while identifying and cutting redundant ad spend.