Ground-truth model

As explained in Causal Inference for Data Science

A ground-truth model is a data-generating model whose relationships and parameter values are treated as known for evaluating an analysis.

A ground-truth model generates data from relationships and parameter values treated as known. It lets you test an analysis against a correct answer before applying the method to situations where the answer is unknown. section 6.1.1

Build a ground-truth analysis by:

• Drawing the relevant variables and arrows between variables that affect one another. section 6.1.1
• Generating synthetic observations from specified relationships, parameters, and random errors. section 6.1.1
• Estimating effects with a candidate method and comparing its results with the known parameters. section 6.1.1

Hotel pricing

Generate 1,000 observations for distance, price, and customers. Set price to decrease with distance and customers to decrease with both price and distance, then test regressions. Adjusting for distance recovers effects near the known values, whereas an unadjusted relationship can reverse direction through Simpson’s paradox. section 6.1.1

Figure 6.1 Graph illustrating the data-generation process for creating synthetic data, as described in the example. The arrows in the graph are labeled with their corresponding linear coefficients, all of which are negative, as this fits the context of the data modeled.

Use the data-generation graph to inspect the variables, directed relationships, and coefficients before simulating observations. section 6.1.1

How Causal Inference for Data Science covers it

How deep it goes

Explains graph construction, data simulation, effect estimation, and comparison with known parameters, stopping short of broader validation guidance.

Examples

Uses a hotel pricing scenario with distance, price, customers, simulated equations, graphs, and adjusted regressions.

What it assumes

Assumes familiarity with variables and linear regression while demonstrating graphing, simulation, and effect estimation through a concrete problem.

What you can do after

Evaluate regression methods by comparing estimated effects with parameters deliberately embedded in synthetic data.

How it connects

Connects causal graphs, synthetic data, linear regression, confounding, and Simpson’s paradox.

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This explainer was generated by Manning's knowledge graph from the text of Causal Inference for Data Science. The section links point to the parts of the book it draws on.