> ## 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 your first causal analysis > A step-by-step walkthrough of asking a causal question, climbing Pearl's ladder, and reviewing your governed claims. This guide walks you through a complete causal analysis session in Wu-Weism — from signing in to reviewing a counterfactual claim in the Claim Ledger. By the end, you will have touched all three rungs of Pearl's causal ladder in a single conversation. ## What you will build A three-question session on cortisol and hippocampal volume that progressively climbs from **association** (Rung 1) to **intervention** (Rung 2) to **counterfactual** (Rung 3), with each response grounded in the neuroscience Structural Causal Model (SCM). Navigate to your Wu-Weism instance and sign in. Once authenticated, go to **/chat**. You will see the Causal Chat interface: a message input at the bottom, the model settings panel in the sidebar, and an empty conversation thread. Before sending your first message, confirm that a provider key is configured — the model selector in the sidebar should show an active provider. If it shows a configuration warning, see [Configuring your AI provider](/guides/ai-providers) first. Type a precise causal question in the message input. Specificity matters — the more clearly you frame a causal relationship, the more accurately Wu-Weism can classify the domain and load the right SCM. ```text Example question — Rung 1 theme={null} Does increased cortisol exposure during stress events causally increase hippocampal volume reduction? ``` Press **Enter** or click **Send**. Frame your question around a specific mechanism rather than a general topic. "Does X causally affect Y under condition Z?" consistently produces better-grounded responses than open-ended questions like "Tell me about cortisol." Before the response content appears, Wu-Weism classifies your question into a research domain. You will see a brief status indicator: ```text theme={null} Classifying domain... Domain: neuroscience Loading SCM truth cartridge... ``` Domain classification determines which Structural Causal Model (SCM) — called a **Truth Cartridge** — is loaded to constrain the response. For this question, the system identifies **neuroscience** and loads the corresponding cartridge, which encodes known causal relationships between stress hormones, the HPA axis, and hippocampal structure. If your question spans multiple domains (e.g., a pharmacological intervention studied in a neuroscience context), Wu-Weism selects the primary domain based on the dependent variable in your question. The response for a Rung 1 (association) question will include: * **Causal density rung**: The response header indicates which rung the answer operates on. For a purely observational question, you will see `Rung 1 — Association`. * **SCM constraints applied**: The specific causal nodes and edges from the neuroscience cartridge that are relevant to your question. * **Governed claim**: A structured claim with an uncertainty label (e.g., `metric-bearing` if empirical data was cited, `structural` if the answer derives from the SCM alone). * **Provenance trace**: The reasoning chain that produced the claim, including which SCM edges were activated. ```text Example response shape theme={null} [Rung 1 — Association | neuroscience SCM] Elevated cortisol during acute stress events is associated with accelerated hippocampal volume reduction, mediated by glucocorticoid receptor activation in CA1 and CA3 subfields. This association is structurally supported by the HPA-axis → hippocampus pathway in the neuroscience SCM. Claim recorded: claim_001 Uncertainty: structural | Confidence: 0.74 ``` The claim ID (e.g., `claim_001`) links to a permanent entry in the [Claim Ledger](/governance/claims). Now escalate to an **intervention** by asking what would happen under a deliberate manipulation. Wu-Weism detects intervention intent from phrasing like "if we intervened," "what would happen if we set," or explicit `do()` notation. ```text Example question — Rung 2 theme={null} What would happen if we intervened to reduce cortisol by 50% during stress events? ``` The response will shift to `Rung 2 — Intervention`: ```text Example response shape theme={null} [Rung 2 — Intervention | do(cortisol = 0.5 × baseline)] Intervening to reduce cortisol by 50% during stress events is estimated to attenuate hippocampal volume reduction by approximately 30–45%, based on the adjustment set {HPA_activity, glucocorticoid_receptor_density}. Identifiability: PASSED — required confounders available in SCM Affected nodes: cortisol → GR_activation → CA1_volume, CA3_volume Delta: −0.38 SD (hippocampal volume reduction, 95% CI: −0.21 to −0.55) Claim recorded: claim_002 Uncertainty: metric-bearing | Confidence: 0.61 ``` Notice that the system reports **identifiability** status. If the required confounders were not available in the SCM, the system would downgrade to association-level and explain which confounders are missing. See [Running interventions](/guides/interventions) for a full explanation of the identifiability gate. Finally, ask a **counterfactual** — a question about what would have happened in an alternate world where a past condition was different. Counterfactuals operate on Rung 3 and require both an observed outcome and a hypothetical antecedent. ```text Example question — Rung 3 theme={null} Would the hippocampal volume reduction have occurred if cortisol levels had been normal throughout? ``` The response will escalate to `Rung 3 — Counterfactual`: ```text Example response shape theme={null} [Rung 3 — Counterfactual | necessity analysis] Under the counterfactual assumption that cortisol remained at baseline (normal range) throughout the stress period, the model estimates that hippocampal volume reduction would not have reached clinical significance in 71% of simulated individuals. Necessity score: 0.71 (cortisol elevation is necessary for observed outcome in most cases) Sufficiency score: 0.43 (cortisol elevation alone is not sufficient — co-activation of the inflammatory pathway is required in ~57% of cases) Claim recorded: claim_003 Uncertainty: metric-bearing | Confidence: 0.58 ``` Necessity and sufficiency scores are standard counterfactual measures from the Pearl framework. A necessity score above 0.5 means the intervention (normal cortisol) would have prevented the outcome in most individuals. Navigate to **/claims** to see the Claim Ledger for this session. All three claims produced in this conversation — `claim_001`, `claim_002`, and `claim_003` — are recorded with: * The rung they were produced on * The SCM cartridge and specific edges activated * Uncertainty label and confidence score * The full provenance trace (the reasoning chain) * Timestamp and session ID Claims are permanent and auditable. You can export them individually or in bulk from the Claim Ledger interface. See [Claim governance](/governance/claims) for details on how to share, challenge, or annotate claims. ## What you covered | Step | Rung | Question type | Key output | | ---------------- | ------ | --------------------- | ------------------------------------------ | | Initial question | Rung 1 | Association | Causal association with SCM support | | Intervention | Rung 2 | `do()` operator | Effect estimate with identifiability check | | Counterfactual | Rung 3 | Necessity/sufficiency | Probability of necessity and sufficiency | ## Next steps * Learn how interventions are evaluated and what happens when identifiability fails: [Running interventions](/guides/interventions) * Analyze supporting literature alongside your causal questions: [Analyzing research papers with PDF upload](/guides/pdf-upload) * Understand how claims are structured and governed: [Claim Ledger](/concepts/claim-ledger)