Can You Trust the Estimate?
Triangulation check
The simulator estimates what happens when one or more factors change. The triangulation check estimates the same scenario a second, independent way — a propensity model built live from the same predictors but relying on the outcome much less directly. That independence is the point. When two methods that rest on different assumptions land close, the result is less likely to be an artifact of one modeling choice. When they diverge, read the estimate cautiously; the cause may be a small sample, weak overlap, residual imbalance, or model dependence.
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Modeling on this page is restricted to binary (yes/no) outcomes.
Auto-Build Actionable found no movable levers for this outcome, so this simulator uses the best Standard model instead — its predictors may not be directly actionable.
Set the scenario
The number on each card is the variable's unique contribution — how much of its standalone influence it keeps once the other variables are in. Variables that move together share their influence, so a higher share means a less redundant, cleaner lever to act on. Cards are ordered most-unique first.
Predicted outcome
How did you change the survey responses?
Each pair of bars compares the actual distribution (green) to the distribution under your scenario (red where you changed it). This is what you changed — not the predicted impact, just the input.
How certain is this result?
Every prediction has wiggle room — these histograms show how much. The green bars are the plausible answers for the baseline; the red bars are the plausible answers for your scenario. Where the colors overlap, the two answers are close enough that the model can't cleanly tell them apart.
Set-everyone-to-X table
Click to show predicted outcomes for every level of every variable
Set-everyone-to-X table
Click to show predicted outcomes for every level of every variable
For each variable's level, the predicted outcome if every respondent had that response, all else unchanged. This is the analytic view of the simulator.
Calibration: predicted vs. observed
How closely the model's predicted probabilities track the observed outcome rates, binned by predicted decile. Bubble size shows respondents per bin. The dashed diagonal is perfect calibration; the blue scenario line is your run; the gray line (when present) is the unmodified base model for comparison.
Explore each factor
For each variable, see how the predicted outcome would change if you switched just that one selection — holding all your other selections fixed. Reveals which individual choices have the most leverage given your current scenario.
Pick things that were already true about people before this answer — age, background, other long-held views. Don't pick something this answer would itself change; that would hide the real effect.
These are the rest of the actionable model. Both methods even the two groups out on them, so the estimates are comparable.
Run a scenario above and we’ll cross-check that exact change here — the simulator’s number beside an independent propensity estimate.