Docs overview

is a no-code causal inference platform that helps data teams move beyond correlations to discover what actually drives their metrics. It currently offers five proven statistical methods—A/B Testing, Difference-in-Differences, Propensity Score Matching, Synthetic Control, and Regression—all accessible through a guided, point-and-click interface. Simply upload your data, select a method, and get clear effect estimates with interactive visualizations.

Explore our documentation below to learn more about each method, when to use it, and how to interpret your results.

Foundations

High-level explanations and conceptual guides.

Why Am I Building ?
The gap between experimentation platforms and real causal inference.
Choosing a Causal Inference Method
Quick guide to when to use A/B Tests, DiD, PSM, or Synthetic Control.
Core causal methods

Concise references for 's supported causal designs.

A/B Tests
Randomized experiments and the gold standard for causal inference.
Linear Regression
When regression works for causal inference, and when it quietly fails.
DiD – Two Point
Two-period DiD for clean before/after changes.
DiD – TWFE
Panel DiD with two-way fixed effects for staggered rollouts.
Propensity Score Matching
Matching treated and control units on their probability of treatment.
Synthetic Control
Building a synthetic counterfactual from donor units.