InterventionFX

Estimate causal impact of interventions using counterfactual time series modeling

Data & AnalyticsPaid

InterventionFX estimates the causal effect of business interventions — product launches, marketing campaigns, policy changes — by constructing counterfactual time series predictions of what would have happened without the intervention. The tool uses Bayesian structural time series models with control time series to build accurate counterfactuals, reporting posterior probability distributions and credible intervals for the estimated effect size. Marketing and product teams use it when randomized experiments aren't feasible but causal claims are needed.

Key Features

• ✓Counterfactual construction
• ✓Bayesian time series modeling
• ✓Control series selection
• ✓Effect size distributions
• ✓Non-experimental causal estimation