How Model-Dependent Is the Estimate?
Triangulation check
The simulator reports what the fitted outcome model implies under a stated scenario. This page re-estimates that scenario through structurally different routes and puts the results side by side. Frederick Mosteller called this triangulation: set up several different models and ask whether differing structures produce similar results.
The propensity route uses propensity-score subclassification built from the same measured predictors rather than the published outcome model. The network route provides a directly comparable estimate when the fitted graph can represent the scenario under the simulator’s reporting rule, with the other factors held at each respondent’s observed values. When a scenario factor reaches the outcome only indirectly, the check instead reports a propagated reading as a different estimand, comparable mainly in direction; when a factor has no path to the outcome, it reports no network estimate.
The routes differ in form while sharing the sample, the measured controls, and the same observational limitations, so agreement narrows one concern only: the result appears less dependent on any single specification. It is not confirmation of a causal effect, and an estimate may be too imprecise to settle the size. When the routes diverge, read the result cautiously; possible reasons include small samples, 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 predictors for this outcome, so this simulator uses the best Standard model instead — its predictors may not be classified as directly movable.
Set the scenario
The number on each card is the variable's incremental contribution — the gain in Tjur R² it adds once the other variables are included, the same quantity reported elsewhere on this site. Bars and ordering use it too, largest first. Hover a number to see how much of the variable's one-at-a-time separation that represents.
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.
Support and calibration
This panel answers two questions. First, is there enough survey data behind your scenario? It shows how many people actually gave the answers you picked, and warns you if any of those answers was given by too few people to trust. Second, how well does the model match the survey?
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. This shows which individual factor settings produce the largest model-implied changes in the current scenario.
Run a scenario above and we’ll cross-check that exact change here — the simulator’s number beside a structurally different propensity estimate.