Name the audience behind an average model effect

Specify the receiving question

The statsmodels 0.15.0 get_margeff reference distinguishes overall, averaging observation-level effects, from mean, evaluating effects at mean regressors. Its dummy option changes a binary input from zero to one rather than treating it as continuous.

Ask the report owner what decision the summary supports and whose experience the sentence is intended to describe. A current subscriber audience, an experimental sample and a proposed future audience are different reporting scopes. Obtain the approved analysis specification rather than choosing a software default because its output has a convenient label.

Keep the audience definition separate from the supplied analytical records. The review can begin with a synthetic example and an aggregate description of eligible profiles; there is no reason to copy identifiable customer histories into a presentation. If the intended audience is not represented in the approved analysis, record that gap for the analyst rather than quietly substituting a convenient export.

Reproduce the profile calculation before summarizing

The statsmodels Logit.cdf reference gives the logistic function s(t) = 1/(1 + exp(−t)). Use an invented model p = s(x + z), with x = 0 and two equally weighted profiles z = −2 and 2. Their probabilities are 0.119203 and 0.880797.

For this model, differentiating with respect to x gives p(1 − p). Both profiles give 0.104994, so their average derivative is 0.104994. At mean z = 0, probability is 0.5 and the derivative is 0.25. These are two different calculations from identical parameters.

Retain the unrounded calculations and the deliberately small profile list beside the report. A second reviewer should be able to reproduce each intermediate result without an account login or a real scoring service. This exercise establishes a discrepancy in the reporting operation. It does not assess the model’s quality, the plausibility of the invented profiles or whether a real audience would respond as predicted.

Choose a meaningful change and aggregation

If x is binary, compare zero with one. The two profile contrasts are s(−1) − s(−2) = 0.149738 and s(3) − s(2) = 0.071777. Their equal-weight average is 0.110758. The contrast at mean z is s(1) − s(0) = 0.231059. Neither is the derivative above.

Write whether the requested summary uses observation-level averaging or a specified profile, and whether the change is a derivative or a discrete contrast. Preserve the chosen audience weights. An average profile is not automatically a real person, and changing the population weights changes the question.

Have the analyst explain what the model can legitimately compare. A report editor should not independently flip a feature that represents an impossible combination, omit required interactions or reinterpret an association as an intervention. Request a supported calculation and its uncertainty for the intended use. Do not invent a confidence interval from the rounded fixture or treat a numerically larger answer as a better model.

Resolve the report as a specification issue

Place the agreed target, eligible audience, input change, aggregation, model version and supplied uncertainty in the report’s definition record. Compare that record with the implementation actually used. If a legacy dashboard followed a different definition, explain which comparisons are affected before presenting a trend. Preserve both calculations with accurate labels when they answer useful but different questions.

Separate a correction to the summary from a change to the model. Replacing a mislabeled report does not itself require refitting the model, and refitting would make it harder to isolate the original discrepancy. If the technical owner later changes the model or audience, evaluate that as a separate revision with its own record and approval.

End with an interpretive sentence approved by the analyst and appropriate to the evidence. A fictional exercise may say that two aggregation procedures disagree; it cannot say that a campaign caused an improvement. The accepted deliverable is a reproducible definition of the requested summary, with unresolved modeling assumptions left visible rather than hidden behind an average.

Make the reasoning concrete.

A fictional publisher receives two summaries from the same model. One analyst describes an audience-average contrast, while another evaluates the change at an average input profile. The editor initially assumes the larger number is a revised campaign effect.

The editor corrects the definition with the analyst and preserves the original model. No real profiles are scored and no targeting decision changes.

Take the questions into your next brief.

Use these prompts in your own notes. Nothing is submitted from this page.

1. Audience

   Which approved population does this summary describe?

2. Change

   What exact input contrast or local derivative is requested?

3. Operation

   Is the effect averaged across profiles or evaluated at a specified profile?

4. Evidence

   Can the calculation and supplied uncertainty be reproduced without changing the model?

5. Wording

   Does the final sentence preserve the model’s associative or causal scope?

Watch for the wrong conclusion.

• Calling an effect at average inputs an audience-average effect.
• Treating a binary change as an infinitesimal derivative without checking the specification.
• Selecting the largest result while leaving its label unchanged.

Sources & scope

All business examples and numerical fixtures are original and fictional. Sources support the identified mathematical or implementation distinctions. Qualified analysts own model specification and inference. No causal result, model fit, actual customer data or operational change is claimed.

• statsmodels 0.15.0: LogitResults.get_margeff →Primary software documentation · Supports: Supports aggregation choices and the binary-input contrast option.
• statsmodels 0.15.0: Logit.cdf →Primary software documentation · Supports: Supports the logistic function; all profiles and numerical calculations are independently constructed.