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On the Actionability of Outcome Prediction
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Theoretical analysis showing that accurate outcome prediction rarely yields the most effective intervention policy, and that measuring actionable/pivotal latent states yields higher action value than predicting the outcome.
Problem
When do accurate outcome predictors help select effective instance-level interventions, given latent states, budgeted measurements, and action costs in social impact settings such as education and healthcare?
Approach
Formalizes prediction value versus action value of measurements in a graphical model of latent states, measurements, outcomes and actions, proving outcome prediction maximizes action value only when a single sufficient action exists, and that pivotal measurements can outperform the outcome itself.
Key results
- In the two-state Boolean example (Y = X1 AND X2), the pivotal measurement X1 AND NOT X2 has the highest action value for all action costs, while the outcome Y has the highest prediction value; the ranking is reversed.
- Theorem: outcome prediction attains maximum action value for any cost c if and only if the outcome has a sufficient action; otherwise a measurement with strictly higher action value exists.
- Proposition: for symmetric monotone Boolean outcomes with i.i.d. Bernoulli states, a constructed measurement M* strictly beats predicting Y in action value for any c in (0,1).
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use the pivotal measurement M_piv instead of the outcome measurement M_Y | Two-state Boolean outcome model · Two-state illustrative example | action value | Helped |
| Use the outcome measurement M_Y instead of the pivotal measurement M_piv | Two-state Boolean outcome model · Two-state illustrative example | prediction value | Helped |
| Use the pivotal measurement M_piv instead of M_Y | Two-state Boolean outcome model · Two-state illustrative example, very low action costs | action value | Helped |
| Use M_Y when actions are very costly | Two-state Boolean outcome model · Two-state illustrative example, very costly actions | action value | No clear effect |
| Replace outcome measurement Y with the pivotal measurement M* | Symmetric monotone Boolean outcome model · General Boolean outcome, single measurement | action value | Helped |
| Use outcome measurement Y when a sufficient action exists | Boolean outcome model · General Boolean outcome, single measurement | action value | Helped |
| Replace outcome measurement Y with a measurement M when no sufficient action exists | Boolean outcome model · General Boolean outcome, single measurement | action value | Helped |
| Replace Y in the measurement set with M* while retaining S | Boolean outcome model · Measurement set containing Y with a relevant, non-sufficient-action case | action value | Helped |
| Include Y alongside X1 ∧ ¬X2 in the measurement set | Two-state Boolean outcome model with P(X1=X2=0)=0 · Modified two-state example | action value | No clear effect |
Surprising findings
- The measurement with the highest prediction value (perfect knowledge of the outcome Y) can have zero action value when actions are costly.
- Outcome prediction is almost never the optimal measurement for interventions, even when combined with other measurements.
Limitations
- Analysis restricted to Boolean outcome functions and binary latent states with a known structural causal model.
- Illustrative examples in education and genomics are sketched informally rather than empirically validated.
- Assumes the planner knows the outcome mechanism g and that actions have fixed known costs.
Experimental setup
Metrics: prediction value action value
Baselines: outcome prediction (perfect outcome measurement M_Y) single-state measurement M_1
Builds on: causal inference do-operator heterogeneous treatment effect estimation algorithmic recourse diagnosis theory
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