Science-backed causal inference for marketing incrementality and budget optimization.
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.
Haus is a leading causal inference platform designed for modern marketing organizations to measure the true incremental impact of their advertising spend.
Explore all tools that specialize in causal impact analysis. This domain focus ensures Haus delivers optimized results for this specific requirement.
Pre-test simulation that calculates the sample size and duration required to detect a statistically significant lift.
Uses machine learning to create a weighted combination of control markets that perfectly mimics the test market's pre-intervention behavior.
Algorithms that identify and remove selection bias and local external factors from geo-test results.
Mapping of variables to show direct and indirect impacts of marketing channels on the final conversion.
Simultaneous measurement of secondary metrics (LTV, retention) alongside primary conversion events.
Monitoring for external market shifts during a live test that could invalidate control groups.
Identifies the halo effect where spend in one channel (e.g., CTV) increases lift in another (e.g., Search).
Connect historical transaction and conversion data via BigQuery, Snowflake, or S3.
Authenticate ad platform integrations (Meta, Google, Amazon, etc.) for spend ingestion.
Define primary KPIs such as Customer Acquisition Cost (CAC) or Return on Ad Spend (ROAS).
Configure market hierarchies for geographical experimental grouping.
Utilize the Haus Market Selection Tool to identify statistically comparable test and control groups.
Set test parameters including duration, budget variance, and confidence intervals.
Execute the experiment live across selected ad platforms through Haus automation.
Monitor real-time p-values and confidence intervals within the Haus dashboard.
Review the final causal lift report to determine the incremental ROAS (iROAS).
Apply findings to global budget allocations using the Haus Optimizer tool.
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"Users praise the scientific rigor and the ability to find 'hidden' savings in ad budgets, though note a steep learning curve for non-data scientists."
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