CAUSAL
TEST SUITE
High-fidelity calibration engine designed to validate attribution integrity against synthetic ground-truth via rigorous MCMC stress testing.
Calibration Protocol
Advanced causal ML framework for psychographic attribution testing
Causal Calibration Protocol
Ensuring mathematical truth by validating models against synthetic datasets where ground truth is known. Calibration testing isolates model bias from true channel performance.
The system injects synthetic 'Ghost Conversions' into data stream to measure the engine's recovery rate. This isolates model bias from true channel performance. A well-calibrated model can distinguish between true incremental lift and noise.
Marketing CFOs and finance teams use calibration tests to validate attribution models before signing off on budget allocations. A passed test suite with 94%+ overall score provides confidence in ROI calculations and prevents systematic attribution errors.
Utilizes No-U-Turn Sampler (NUTS) for Bayesian posterior estimation. Divergence monitoring ensures geometry of posterior space is well-behaved. The calibration framework generates synthetic data following same causal structure as real data, then measures recovery of known treatment effects.
Calibration_Diagnostics
Last-Touch Bias
Markov-Bayes Decomposition
Correlated Channels
Hierarchical Correlation Matrix
Interaction Effects
Multi-Variate Non-Linear Ensembles
Delayed Effects
Adstock Decay Calibration
Confounding Variables
Latent Variable Modeling