Impact overview
Bring budgets, reach, measured outcomes and attribution assumptions into one transparent decision workspace.
Measure what matters.
Explain what you assume.
Compare program effectiveness without presenting modeled outcomes as verified causal impact.
Incremental outcomes by program
Illustrative incremental outcomes; negative results are retained
Budget allocation
Top active programs by assumed total budget
Program effectiveness
Illustrative cost and incremental outcome comparisons
| Program | Budget | Reach | Incremental | Cost / outcome |
|---|
Interpretation guide
What the portfolio numbers can and cannot tell you
Before-and-after differences may reflect confounding, selection and other factors.
A percentage chosen by the user is not established by this simulator.
Harmful or deteriorating results are not concealed in aggregates.
Scenario studio
Stress-test how program coverage, measured change, costs and assumed attribution affect impact efficiency.
Scenario controls
Changes apply to all active programs; original data remains unchanged
Scenario outcomes
Relative to the unchanged reference portfolio
Net incremental outcomes
Baseline vs selected scenario
Detailed comparison
Negative incremental results are possible and shown as negative values
| Program | Scenario reach | Budget | Incremental outcomes | Cost per positive outcome |
|---|
Program registry
Edit impact programs and assumptions. Archiving keeps each record in your backup rather than deleting it.
| Program | Sector | Budget ($) | Reach | Pre / Post | Attribution | Evidence | Actions |
|---|
Uncertainty lab
Reproducible illustrative Monte Carlo draws reveal how sensitive modeled outcomes are to assumptions.
Uncertainty distribution
400 deterministic draws · illustrative net incremental outcomes
Priorities & limitations
Signals to investigate before reporting real impact
Program outcome sensitivity
Assumed incremental outcomes under ±10pp attribution
How draws are constructed
Each draw changes projected participants around the current scenario using bounded multiplicative shocks.
Follow-up outcome rates receive bounded synthetic variation, with valid rate limits.
Attribution assumptions fluctuate, but draws are not calibrated uncertainty intervals.
Methodology
Inspect the exact logic and limitations behind each score. A simulation does not substitute a real evaluation design.
Impact equations
Change = follow-up outcome rate − baseline outcome rate, expressed in percentage points.
Incremental = reach × (follow-up − baseline)/100 × assumed attribution/100. Negative outcomes are preserved.
Cost per positive incremental outcome = budget ÷ positive incremental outcomes. Undefined when net additional outcomes ≤ 0.
Aggregate budgets, reach and signed incremental outcomes. Programs are assumed non-overlapping for summary purposes.
Scope boundaries
No randomized design, control group adjustment, confounder identification or counterfactual estimation.
Different outcome types should not be treated as inherently comparable; the demo assumes standardized outcome units.
Browser local storage is not encrypted. Use fictional or non-sensitive data.
HTML, CSS and JavaScript. No API keys, analytics, paid services, external scripts or network requests.
Demo program catalog
All assumptions are editable; no figures represent real-world research findings