Causal Inference Engine

beyond-correlation

Beyond Correlation A/B testing requires randomization. But most business decisions aren't randomly assigned — you launched a feature to everyone, changed pricing for a segment, or redesigned a page for a campaign. How do you measure causal impact without a control group? The Causal Inference Engine uses quasi-experimental statistical methods. Difference-in-differences compares treatment and control groups over time. Synthetic control constructs a counterfactual from weighted combinations of untreated units. Regression discontinuity exploits threshold-based assignment. These methods give you causal answers from observational data.

Causal Inference Specifications

Methods

Difference-in-differences, synthetic control, regression discontinuity, instrumental variables

Outputs

Causal effect estimates with confidence intervals

Validation

Placebo tests, pre-trend analysis, sensitivity analysis

Visualization

Counterfactual plots with treatment effect bands

features

diff in diff: true

synthetic control: true

regression discontinuity: true

Relationships