> ## Documentation Index > Fetch the complete documentation index at: https://wuweism.com/llms.txt > Use this file to discover all available pages before exploring further. # Running interventions with do-calculus > How to phrase intervention questions, understand the identifiability gate, and read intervention results in Wu-Weism. An **intervention** in causal inference is fundamentally different from an observation. When you observe that two variables are correlated, you are on Rung 1 of Pearl's causal ladder. When you ask what would happen if you actively *set* a variable to a specific value — regardless of its natural causes — you are on Rung 2, and you need do-calculus to answer the question correctly. Wu-Weism operationalizes Rung 2 through an **intervention pipeline** that checks identifiability before computing effects, so you always know whether your result is causally valid or association-level. ## Observations vs. interventions | | Observation (Rung 1) | Intervention (Rung 2) | | ------------------------- | ------------------------- | ------------------------------------------- | | **Question form** | "Is X associated with Y?" | "What happens to Y if we set X to value v?" | | **Notation** | P(Y \| X = x) | P(Y \| do(X = x)) | | **Confounders** | Can distort the estimate | Must be accounted for via adjustment set | | **Identifiability check** | Not required | Required before computing effect | | **Claim rung** | Rung 1 | Rung 2 | The distinction matters because confounders can make an observational association point in the wrong direction or overstate the effect size. The `do()` operator severs the incoming arrows to X in the causal graph, asking: if we forcibly set X (ignoring whatever normally determines it), what does Y become? ## Phrasing intervention questions Wu-Weism detects intervention intent from specific language patterns. Any of the following will trigger Rung 2 processing: ```text do() notation theme={null} If we do(anti_inflammatory_drug = 1), what happens to IL-6 levels? ``` ```text Explicit intervention language theme={null} If we intervene to set TNF-α to zero, how does CRP respond? ``` ```text Set/assign framing theme={null} What would happen if we set cortisol to 50% of baseline during the stress window? ``` ```text Treatment framing theme={null} What is the effect of administering the treatment on downstream inflammation markers? ``` ```text Confounder/mediator framing theme={null} Holding confounders constant, what is the direct effect of IL-6 on fibrosis? ``` **Keywords that reliably trigger Rung 2**: `do(`, `intervention`, `intervene`, `if we set`, `treatment`, `confounder`, `mediator`, `identifiability` The most unambiguous phrasing is explicit `do()` notation: `do(variable_name = value)`. This removes any ambiguity between "if we observed X at level v" (Rung 1) and "if we set X to level v" (Rung 2). ## Using operator mode In addition to natural language triggers, you can force Rung 2 mode from **Model Settings** in the workbench sidebar: Click the **Model Settings** icon in the left sidebar of the Causal Chat interface. Under **Inference Mode**, locate the **Operator** dropdown. The default is **Auto** (Wu-Weism selects the rung based on your question). Set the operator mode to **Intervene**. This pins all subsequent questions in the session to Rung 2, even if your phrasing is observational. Operator mode is useful when you are systematically exploring a causal graph and want every question treated as an intervention query, without needing to include `do()` notation each time. ## The identifiability gate Before Wu-Weism computes any intervention effect, it runs an **identifiability check**. Identifiability means: given the causal graph (SCM) and the available data, can the interventional distribution `P(Y | do(X))` be computed from observational data alone? If the required confounders are not represented in the loaded SCM, the effect is **not identifiable** and the computation cannot be trusted. ### When identifiability passes The system proceeds to compute the intervention effect using the adjustment set — the minimal set of variables you need to condition on to block all backdoor paths from X to Y. ```text Identifiability: PASSED theme={null} Identifiability: PASSED Adjustment set: {age, baseline_inflammation, bmi} Proceeding with do-calculus computation... ``` ### When identifiability fails If the identifiability gate blocks the computation, Wu-Weism does not silently return a biased estimate. Instead, it: 1. Downgrades the response to association-level (Rung 1) 2. Explains which confounders are missing from the SCM 3. Labels the claim with `association-only` so it is not mistaken for a causal result ```text Identifiability: BLOCKED theme={null} Intervention request downgraded: required confounders are missing. Returning association-level answer. Missing from SCM: {genetic_IL6_variant, prior_infection_history} To run this intervention, these variables must be present in your adjustment set or representable in the loaded SCM. ``` This behavior is intentional. A Rung 1 answer that is clearly labeled as such is more scientifically useful than a Rung 2 answer that is secretly confounded. ## Worked example: drug intervention on inflammation **Research scenario**: You are studying whether administering an anti-inflammatory drug reduces interleukin-6 (IL-6) levels in patients with chronic inflammatory disease. You want a causal estimate, not just a correlation. **Step 1 — Ask the intervention question** ```text theme={null} If we do(anti_inflammatory_drug = 1), what happens to IL-6 levels in patients with baseline CRP > 10 mg/L? ``` **Step 2 — System event flow** ```text Event sequence theme={null} domain_classified → immunology scm_loaded → immunology-inflammation-v3 intervention_gate → PASSED (adjustment set: {age, baseline_CRP, disease_duration}) intervention_effect → computed via backdoor adjustment answer → Rung 2 response with delta and affected nodes ``` **Step 3 — Read the intervention result** ```text Example response shape theme={null} [Rung 2 — Intervention | do(anti_inflammatory_drug = 1)] Administering anti_inflammatory_drug (set to active) reduces IL-6 levels by an estimated 42% (95% CI: 31–53%) in patients with baseline CRP > 10 mg/L. Adjustment set applied: {age, baseline_CRP, disease_duration} Identifiability: PASSED Affected nodes: anti_inflammatory_drug → NF-κB_inhibition → IL-6_synthesis anti_inflammatory_drug → COX2_suppression → PGE2 → IL-6_amplification Delta: −42% IL-6 (mean), −0.81 SD Causal trace persisted: trace_007 Claim recorded: claim_019 Uncertainty: metric-bearing | Confidence: 0.68 ``` ## Intervention payload structure Internally, Wu-Weism represents every intervention as a structured payload. Understanding this helps you interpret what the system is computing: | Field | Description | | ------------------- | ----------------------------------------------------------------------------------- | | `node_id` | The variable being intervened on (e.g., `anti_inflammatory_drug`) | | `value` | The value it is set to (e.g., `1` for active, `0` for inactive, or a numeric level) | | `outcome_var` | The downstream variable you are measuring (e.g., `IL6_level`) | | `adjustment_set` | The confounders conditioned on to block backdoor paths | | `known_confounders` | All confounders present in the loaded SCM for this domain | You can also supply these fields directly via the [Causal Chat API](/api/overview) if you are building programmatic workflows. ## Viewing the causal trace Every successful intervention produces a **causal trace** — a record of which graph edges were activated, which adjustment variables were used, and how the effect propagated through the SCM. You can view the causal trace in two places: * **In the Claim Ledger** at `/claims`: locate the claim by ID (e.g., `claim_019`), then expand the **Provenance** section. The trace ID (e.g., `trace_007`) links to the full edge-by-edge record. * **Via the API**: use the `trace_id` from the claim to query `/api/scm/counterfactual-traces/{traceId}` and retrieve the full trace record as JSON for downstream analysis or audit. ## Common mistakes to avoid **Asking "what if we observed X at level v?" instead of "what if we set X to v?"** These are different questions. The first is observational (Rung 1). The second is interventional (Rung 2). When in doubt, use explicit `do()` notation. **Ignoring the identifiability gate result** If identifiability fails and the response is labeled `association-only`, do not treat the estimate as causal. The downgrade is a signal that the SCM is missing information needed for a valid Rung 2 answer. **Assuming a large effect size means identifiability passed** Effect size and identifiability are independent. A large association can exist even when the causal effect is not identifiable from available data. ## Next steps * Ask counterfactual questions after an intervention: [Concepts — Counterfactuals](/concepts/counterfactuals) * Attach supporting literature to your intervention questions: [Analyzing research papers with PDF upload](/guides/pdf-upload) * Review governed intervention claims: [Claim Ledger](/concepts/claim-ledger)