# Causal Inference Engine

Canonical URL: https://babadook.tech/modules/causal-inference
Entity: modules
Content hash: 447cb04f171fd95ef1163f77204c0343cf43ef2a2747f8bc20e41edb6a6911b8

Quasi-experimental statistical methods for determining true cause-and-effect from observational data — difference-in-differences, synthetic control, and regression discontinuity.

Quasi-experimental statistical methods for determining true cause-and-effect from observational data — difference-in-differences, synthetic control, and regression discontinuity.

## 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.

### data Viz

## 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

### products

- DXP — https://babadook.tech/products/dxp

### capabilities

- Advanced Analytics — https://babadook.tech/capabilities/advanced-analytics

### sub modules

- Analytics & Intelligence — https://babadook.tech/sub-modules/analytics-intelligence

### use cases

- Behavioral Analytics
- Feature Experimentation