> ## Documentation Index > Fetch the complete documentation index at: https://wuweism.com/llms.txt > Use this file to discover all available pages before exploring further. # Legal Causation > Causation analysis meeting legal epistemological standards, applying but-for and proximate cause frameworks. The Legal Causation surface applies the same SCM-backed causal reasoning engine used across Wu-Weism, but structured around the epistemological standards that courts and regulatory bodies demand. Where general causal dialogue operates at the level of scientific inference, Legal Causation operates at the level of legal sufficiency: what a factfinder needs to establish causation under existing legal frameworks. Navigate to Legal Causation by selecting **Legal Causation** in the sidebar, or by opening `/legal` directly. ## What it does Legal Causation answers two kinds of causal questions that recur across law and regulation: 1. **But-for causation**: Would the harm have occurred but for the defendant's conduct? This is the foundational test in most negligence and tort analysis. 2. **Proximate cause analysis**: Given a causal chain, where does legal responsibility begin and end? This matters in multi-factor scenarios where several actors or events contributed to an outcome. Both analyses are grounded in explicit causal chain modeling — not narrative reasoning. The system builds a directed causal graph over the facts you provide, assigns confidence levels to each edge, and returns structured output that shows the reasoning, not just the conclusion. ## Running an analysis In the scenario field, describe the factual situation in plain language. Include the relevant actors, actions, events, and the harm or outcome at issue. You do not need to use legal terminology — describe what happened. Example: *"A pharmaceutical manufacturer distributed a drug without updating the warning label after internal studies showed elevated cardiac risk in patients over 60. A 67-year-old patient suffered a cardiac event after taking the drug for three months."* In the legal question field, state the specific causal question you need answered. Example: *"Was the manufacturer's failure to update the warning label a but-for cause of the patient's cardiac event?"* Click **Analyze**. The system builds a causal chain over your scenario, evaluates the but-for condition, and traces proximate cause where applicable. The output includes a causal chain diagram, confidence levels for each causal link, the but-for conclusion, and proximate cause boundaries where relevant. ## Causal frameworks applied **Standard**: Would the harm have occurred absent the defendant's conduct? The but-for test is evaluated by counterfactual comparison: the system constructs the factual world as described, then constructs a counterfactual world in which the identified conduct did not occur, and compares outcomes across both. Output includes: * The but-for conclusion (yes / no / indeterminate) * Confidence level for the conclusion * The specific factual premises required to support the conclusion * Sensitivity analysis: which facts, if changed, would alter the conclusion When multiple sufficient causes are present (each independently capable of producing the harm), the but-for test can return indeterminate. The output will flag this and describe the alternative causation structure. **Standard**: Was the conduct a legally significant cause — was the harm a foreseeable result within the risk that made the conduct wrongful? Proximate cause analysis traces the full causal chain from conduct to harm, identifies intervening actors or events, and evaluates whether each link was foreseeable given the risk profile of the original conduct. Output includes: * The causal chain as a directed graph with edge confidence levels * Identification of any superseding causes that may break the chain * A proximate cause conclusion with supporting reasoning * Per-actor contribution analysis in multi-party scenarios ## Output structure A completed legal causation analysis returns: * **Causal chain summary**: a plain-language description of the chain from conduct to harm * **Causal graph**: a structured representation of each causal link with confidence levels * **But-for conclusion**: the counterfactual result and its confidence level * **Proximate cause boundary**: where the chain of legally cognizable causation begins and ends * **Assumptions**: the factual premises the analysis depends on — reviewable and challengeable All output is governed and traceable. The analysis can be exported and referenced in legal memoranda, expert reports, or regulatory submissions. ## Use cases Evaluate whether a design defect, manufacturing defect, or failure to warn was a but-for cause of a plaintiff's injury. Particularly useful in pharmaceutical, medical device, and consumer product contexts where the causal chain runs through multiple intermediaries (prescribers, retailers, users). Assess whether a defendant's breach of duty was a proximate cause of the plaintiff's harm. Supports multi-factor negligence scenarios involving comparative fault and contribution among multiple defendants. Determine whether a regulatory violation was causally connected to a downstream harm — relevant in enforcement actions, compliance audits, and penalty calculations where causal nexus must be established to justify a sanction. Analyze causal contribution across multiple events or parties to support coverage determinations, subrogation analysis, or indemnification disputes. Legal Causation provides structured causal analysis, not legal advice. Conclusions drawn from this tool should be reviewed by qualified legal counsel before use in proceedings, filings, or formal legal opinions. ## Related pages Run SCM-grounded causal dialogue in the `legal` domain for exploratory analysis. Understand how Wu-Weism constructs and evaluates counterfactual worlds. Record and govern causal conclusions for audit and citation. Track causal reasoning quality and alignment across your analyses.