Intervention validation

Before running a causal analysis, use this endpoint to check whether the effect of an intervention on an outcome is identifiable — that is, whether it can be estimated from observational data given your adjustment strategy. The endpoint checks your adjustment set against the SCM’s causal graph and reports whether the back-door (or other) criterion is satisfied.

Validate an intervention

POST /api/scm/intervention/validate

This endpoint requires authentication. Include your session token in the Authorization header.

Request body

string

required

The variable you intend to intervene on (the “do” variable in Pearl’s do-calculus). Must be a node in the target SCM.

string

required

The outcome variable whose causal response you want to estimate.

string[]

Variables you plan to control for (condition on) in your analysis. The endpoint checks whether this set is sufficient to block all back-door paths from treatment to outcome.

string[]

Confounders you have identified from domain knowledge. Used alongside the SCM structure to assess completeness of your adjustment set.

string

The SCM to validate against. If omitted, Wu-Weism selects the most relevant registered model based on the variable names you provide. See SCM model registry for available model keys.

Response fields

boolean

required

true if the intervention is identifiable with the provided adjustment set. false if the effect cannot be estimated without additional variables or structural assumptions.

string

required

The output class permitted by the identifiability analysis. One of:

interventional — the effect of the do-intervention P(Y | do(X)) is estimable.
observational — only observational associations P(Y | X) are estimable; intervention is not identified.
blocked — the intervention violates structural constraints in the SCM and cannot proceed.

string

required

Human-readable explanation of the identifiability decision.

object

required

Detailed identifiability analysis.

Hide identifiability properties

boolean

Whether the causal effect is identifiable from observational data.

string[]

The complete set of variables that must be adjusted for to identify the effect.

string[]

The valid adjustment set confirmed by the SCM analysis. May differ from the set you supplied if the SCM identifies a minimal sufficient set.

string[]

Variables present in requiredConfounders that are absent from your supplied adjustmentSet. An empty array means your adjustment strategy is sufficient.

string

Additional notes on the criterion satisfied (e.g., back-door, front-door, IV) or the reason the effect is not identified.

Example

curl --request POST \
  --url https://wuweism.com/api/scm/intervention/validate \
  --header 'Authorization: Bearer <token>' \
  --header 'Content-Type: application/json' \
  --data '{
    "treatment": "cortisol_level",
    "outcome": "hippocampal_volume",
    "knownConfounders": ["stress_exposure", "age"],
    "adjustmentSet": ["stress_exposure"],
    "modelKey": "neural_topology_v1"
  }'

const response = await fetch('https://wuweism.com/api/scm/intervention/validate', {
  method: 'POST',
  headers: {
    'Authorization': 'Bearer <token>',
    'Content-Type': 'application/json',
  },
  body: JSON.stringify({
    treatment: 'cortisol_level',
    outcome: 'hippocampal_volume',
    knownConfounders: ['stress_exposure', 'age'],
    adjustmentSet: ['stress_exposure'],
    modelKey: 'neural_topology_v1',
  }),
});

const result = await response.json();

import requests

response = requests.post(
    'https://wuweism.com/api/scm/intervention/validate',
    headers={'Authorization': 'Bearer <token>'},
    json={
        'treatment': 'cortisol_level',
        'outcome': 'hippocampal_volume',
        'knownConfounders': ['stress_exposure', 'age'],
        'adjustmentSet': ['stress_exposure'],
        'modelKey': 'neural_topology_v1',
    },
)

result = response.json()

{
  "allowed": true,
  "allowedOutputClass": "interventional",
  "rationale": "Sufficient adjustment set identified.",
  "identifiability": {
    "identifiable": true,
    "requiredConfounders": ["stress_exposure"],
    "adjustmentSet": ["stress_exposure"],
    "missingConfounders": [],
    "note": "Back-door criterion satisfied."
  }
}

{
  "allowed": false,
  "allowedOutputClass": "observational",
  "rationale": "The adjustment set does not block all back-door paths. Conditioning on 'stress_exposure' alone is insufficient; 'age' must also be included.",
  "identifiability": {
    "identifiable": false,
    "requiredConfounders": ["stress_exposure", "age"],
    "adjustmentSet": ["stress_exposure"],
    "missingConfounders": ["age"],
    "note": "Open back-door path exists through 'age'. Add 'age' to your adjustmentSet to satisfy the back-door criterion."
  }
}

{
  "error": "Treatment variable 'cortisol_level' is not a node in model 'alignment_bias_scm'."
}

A response of "allowed": false does not mean the causal effect does not exist — it means the effect cannot be estimated from observational data with your current adjustment strategy. You may need to collect additional data, add variables to your adjustment set, or apply an alternative identification strategy (front-door criterion, instrumental variables, etc.).

Overview

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Claims & Synthesis

Intervention validation

Validate an intervention

Request body

Response fields

Example

​Validate an intervention

​Request body

​Response fields

​Example

Validate an intervention

Request body

Response fields

Example