Free-form notebook — experiment with data & AI
A Jupyter-style environment for data exploration, statistical analysis, and research design validation — powered by Claude AI.
Interactive code cells with AI assistance
Write
Python code cells with pandas, numpy, statsmodels, scikit-learn, scipy. Markdown cells for notes.
Run
Execute cells with one click. Output appears inline — regression tables, summary stats, print output.
Interpret
Add AI cells below any code cell. Claude reads your code and output, gives plain-language interpretation.
7 Templates
Workflow
Monte Carlo validation of research designs
Before collecting real data, validate your research design with Monte Carlo simulations. Claude generates and executes Python code (500-5000 iterations), then interprets the results. Available when creating a new notebook via the template picker.
Randomized Controlled Trial
Simulate a two-group experiment. Check statistical power, minimum sample size, Type I error rate, and effect estimate accuracy.
“Am I powered enough to detect my expected treatment effect?”
Difference-in-Differences
Panel with treatment/control groups, pre/post periods. Test parallel trends assumption and quantify bias if it fails.
“Will my DiD estimate be biased if pre-trends aren't perfectly parallel?”
Sample Bias Analysis
Model selection bias, survivorship bias, or differential attrition. Compare naive OLS against IPW-corrected estimates.
“How bad is my attrition problem? Does IPW fix it?”
Power Analysis
Grid search over effect sizes and sample sizes. Find the smallest N that achieves 80% power for your minimum detectable effect.
“What sample size do I need for my grant proposal?”
Synthetic Control
Generate multi-country panel data with one treated unit. Validate whether synthetic control recovers the true effect.
“Can synthetic control work with my donor pool size?”
Generate realistic experimental datasets
Build a simulated population, design an intervention, configure a timeline — then generate a full panel dataset with realistic demographics, treatment effects, non-compliance, spillovers, and non-random attrition. Access it from the Simulation World tab above.
Step 1: Population
Set country, size, age/income distributions, urban fraction, baseline health scores.
Step 2: Intervention
Choose assignment (random/geographic/eligibility), effect size, compliance rate, spillover.
Step 3: Timeline
Baseline year, follow-up waves, intervention start, attrition rate, attrition bias.
Output
A long-format panel CSV with columns: id, year, treatment, age, female, urban, income, health_score, attrited, wave. Download as CSV, push to a notebook, or view a preview table directly in the UI.
Context-aware research assistant
When working in a notebook, switch to the AI Chat tab in the right panel. The AI reads all cell outputs as context, so you can ask questions like: