Guided wizard
5 steps from data upload to plain-English results. No statistics PhD required.
Causal inference features
Everything you need for rigorous causal analysis — without enterprise software prices or writing Python notebooks.
Propensity score matching
Match treated and control units on covariates — standard for observational medicine.
Difference-in-differences
Evaluate policies with before/after treated vs control designs.
Double ML & causal forests
Machine learning treatment effects with EconML — heterogeneous effects by subgroup.
DoWhy integration
Explicit causal graphs, backdoor adjustment, and refutation tests.
Balance diagnostics
Love-plot style standardized mean difference checks before you trust results.
Robustness checks
Placebo outcomes, random confounders, subsample validation.
Uplift targeting
Per-user treatment effect scores and CSV export for marketing campaigns.
What-if scenarios
Counterfactual simulator: what if treatment rate changed?
Causal discovery
PC algorithm and LiNGAM to learn structure from data.
AI assistant
Suggests variables, methods, and explains results in plain English.
Export & reports
Download HTML reports, Python scripts, print to PDF for stakeholders.
Start with a 14-day Pro trial
Full methods, exports, and AI summaries. No credit card. Then stay on Free or upgrade.