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Stable unit treatment value assumption

As explained in Causal Inference for Data Science

The stable unit treatment value assumption (SUTVA) requires treatment versions to be well defined and one unit’s treatment not to affect another unit’s outcome.

Before comparing prices or treatments, ask whether each treatment has one clear meaning and whether one unit’s treatment can change another unit’s outcome. SUTVA requires well-defined treatment versions and no interference between units. section 10.1.4

Check SUTVA through two practical questions:

  • Are treatment versions interchangeable? Surgery performed by different doctors or using different techniques may represent different interventions. section 10.1.4 section 10.2.2
  • Can one unit’s treatment affect another unit’s outcome? If so, outcomes depend on other units’ treatment assignments, creating interference. section 10.1.4

Competing bike prices

Randomly changing the prices of competing bike models A and B does not isolate each price’s effect if lowering A’s price reduces purchases of B. The units interfere, so ordinary potential-outcome comparisons violate SUTVA and require specialized methods. section 10.1.4 section 10.2.2

Figure 10.2 Price effect on competing products. There is interference between prices, so we cannot assume SUTVA.
figure

Use the figure to visualize how prices of competing products interfere with one another. section 10.2.2

DAGs handle treatment versions more explicitly, and graphical approaches do not automatically assume SUTVA; interference instead affects how interventions represented by the do operator are interpreted. section 10.2.2

How Causal Inference for Data Science covers it

How deep it goes
Defines treatment versions and interference, then works through competing prices and contrasts their handling in DAGs.
Examples
Uses competing bike prices and surgeon-specific techniques to expose interference and ambiguous treatment versions.
What it assumes
Assumes potential outcomes and treatment assignments, while defining well-defined interventions and interference within that framework.
What you can do after
Evaluate whether a causal comparison has clear treatment versions and independent units before interpreting its effect.
How it connects
Connects potential outcomes, well-defined interventions, DAGs, and the do operator through the problem of interference.

Related concepts

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.