Most market analysis works backwards. A stock moves, and analysts explain why. A commodity spikes, and traders identify the catalyst after the fact. This reactive approach leaves a critical gap: the time between when a structural event occurs and when the market fully prices its consequences. At Nucigen Labs, we call this gap the "alpha window" — and causal chain analysis is how we find it. A causal chain is a sequence of cause-and-effect relationships that connect an upstream event to downstream market consequences. Unlike correlation-based models that identify statistical relationships between variables, causal chains map the directional flow of real-world impact: a mining disruption causes a raw material shortage, which causes manufacturing delays, which causes inventory drawdowns, which causes margin compression, which causes earnings misses, which causes repricing. Each link in this chain has a measurable time delay and a quantifiable probability of propagation. The chain is not a metaphor — it is a structured, computable model. Traditional financial analysis operates at the asset level. It asks: "What will this stock do?" Causal chain analysis operates at the system level. It asks: "What structural constraints have changed, and how will those changes propagate through interconnected industries?" The difference matters because markets are not efficient at pricing multi-tier propagation. Tier 1 effects (direct exposure) are priced quickly, often within hours. But Tier 3 and Tier 4 effects — the indirect, second-order consequences that ripple through supply chains, logistics networks, and regulatory environments — can take weeks or months to materialize. This is where the edge exists. Our system ingests global news and structured data from over 140 countries, extracts entities and actions using natural language processing, and classifies events into a taxonomy of structural change types: sanctions, trade restrictions, production disruptions, regulatory shifts, and geopolitical realignments. From there, we construct directed acyclic graphs (DAGs) that model how each event type propagates through industrial tiers. The output is not a prediction about a single stock — it is a map of structural consequences with estimated timing, magnitude, and confidence at each tier. Every falsifiable claim an analysis makes is written to a public ledger with a probability and an evaluation date, and scored against real evidence when it comes due. That ledger has not yet resolved a single claim, so Nucigen publishes no accuracy rate, no hit rate and no directional-accuracy figure — a rate over an empty sample is not a small claim, it is a fabricated one. The ledger, including its current emptiness and the date the first claim comes due, is public at /track-record.