Data ScientistExplainabilityIntermediateSingle prompt

Counterfactual Explanations AI Prompt

This prompt generates actionable counterfactual explanations for unfavorable model outcomes. It is useful in domains where people need to understand what realistic changes could improve their predicted outcome. The focus is on minimal, feasible, and user-actionable changes rather than impossible edits.

Prompt text
Generate counterfactual explanations for rejected or unfavorable predictions from this model.

A counterfactual answers the question: 'What is the minimal change to the input that would flip the prediction?'

For the top 10 most impactful negative predictions (e.g. loan rejected, churn predicted, fraud flagged):
1. Find the nearest counterfactual: the smallest change to input features that would result in a positive prediction
2. Constraints: only change features that are actionable (not age, not historical data — only things the person can change)
3. For each counterfactual show: original values | counterfactual values | what changed | magnitude of change
4. Rank the required changes from easiest to hardest to achieve
5. Generate a plain-English 'what you could do differently' explanation for each case

Return: counterfactual table for each case and template text suitable for a customer-facing explanation.

When to use this prompt

Use case 01

You need explanations for rejected, flagged, or otherwise unfavorable predictions.

Use case 02

The audience cares about what can be changed, not just why the result happened.

Use case 03

Only actionable features should be modified in the explanation.

Use case 04

You want outputs suitable for customer-facing or operations-facing use.

What the AI should return

Counterfactual tables for each selected case, ranked actionable changes, and plain-English explanation templates describing what changes could flip the prediction.

How to use this prompt

1

Open your data context

Load your dataset, notebook, or working environment so the AI can operate on the actual project context.

2

Copy the prompt text

Use the copy button above and paste the prompt into the AI assistant or prompt input area.

3

Review the output critically

Check whether the result matches your data, assumptions, and desired format before moving on.

4

Chain into the next prompt

Once you have the first result, continue deeper with related prompts in Explainability.

Frequently asked questions

What does the Counterfactual Explanations prompt do?+

It gives you a structured explainability starting point for data scientist work and helps you move faster without starting from a blank page.

Who is this prompt for?+

It is designed for data scientist workflows and marked as intermediate, so it works well as a guided starting point for that level of experience.

What type of prompt is this?+

Counterfactual Explanations is a single prompt. You can copy it as-is, adapt it, or use it as one step inside a larger workflow.

Can I use this outside MLJAR Studio?+

Yes. The prompt text works in other AI tools too, but MLJAR Studio is the best fit when you want local execution, visible Python code, and reusable notebooks.

What should I open next?+

Natural next steps from here are Decision Tree Proxy, Feature Importance, Full XAI Chain.