Simple Functions
GLOSSARY/ANALYSIS

Causal Tree

A causal tree is a structured probabilistic model that breaks down a complex event into a hierarchy of independent, verifiable sub-conditions. Each node has a probability, and the tree computes the overall probability of the root event.

CLI:sf thesis view --tree

Causal Trees: SimpleFunctions' Core Analytical Framework

A causal tree is how SimpleFunctions thinks about prediction markets. Instead of asking "what's the probability of X?" directly, we decompose the question into smaller, more answerable components.

Structure

A causal tree has:

  • Root node: The ultimate question (e.g., "Will Iran-Israel conflict escalate to direct confrontation?")
  • Branch nodes: Conditions that contribute to the root outcome
  • Leaf nodes: Observable, verifiable facts that can be tracked

Example: Iran Conflict Thesis

Root: Iran-Israel direct confrontation (p = 35%)
├── n1: Iran retaliates beyond proxies (p = 45%)
│   ├── n1.1: Missile/drone strike on Israeli territory (p = 60%)
│   └── n1.2: Naval confrontation in Strait of Hormuz (p = 30%)
├── n2: US military involvement (p = 25%)
│   ├── n2.1: US bases in region attacked (p = 40%)
│   └── n2.2: Congressional authorization (p = 15%)
└── n3: Diplomatic channels fail (p = 55%)
    ├── n3.1: UN Security Council deadlocked (p = 70%)
    └── n3.2: Back-channel negotiations collapse (p = 45%)

Why Trees Beat Single Estimates

  1. Decomposition makes estimation easier: Estimating "probability of WWIII" is impossible. Estimating "probability of UN Security Council deadlock on Iran resolution" is tractable.
  2. Information maps to nodes: A headline about Iranian missile tests maps directly to node n1.1. Without the tree, you'd have to reason about how it affects the overall probability — with the tree, you update one node and the math propagates.
  3. Confidence tracking: Each node has both a probability and a confidence level. If n3.2 has low confidence, you know where to focus research.

The Agent Uses Trees for Everything

When the heartbeat service runs, it:

  1. Scans for new information (news, price changes)
  2. Maps each piece to a causal node
  3. Updates node probabilities
  4. Recomputes the root probability
  5. Compares against market prices to find edge
  6. Triggers strategy actions if conditions are met

Example

sf thesis view --tree

Thesis: "US enters recession by end of 2026"
Root probability: 42%

Causal tree:
n1: Labor market deterioration        p=55%  conf=72%
  n1.1: Unemployment > 5%             p=35%  conf=68%
  n1.2: Non-farm payrolls < 100K/mo   p=40%  conf=65%
n2: Consumer spending decline          p=45%  conf=70%
  n2.1: Real retail sales negative     p=38%  conf=75%
  n2.2: Consumer confidence < 80       p=52%  conf=60%
n3: Fed policy error                   p=30%  conf=55%
  n3.1: Overtightening (rates > 5.5%)  p=20%  conf=50%
  n3.2: Too late to cut                p=40%  conf=58%

Root = weighted combination of branches
Edge on KXRECSSNBER-26: 42% thesis - 28% market = +14pt

Related Terms

Thesis Confidence Score

Confidence is a 0-100% score that measures how certain you are about your probability estimates, not the probability itself. High confidence means your estimates are well-researched; low confidence means they're speculative.

Thesis

A thesis is a structured investment argument that combines a directional view, a causal model of why the view is correct, and specific market positions that express the view. In SimpleFunctions, a thesis is the core unit of analysis.

Thesis-Implied Price

The thesis-implied price is the probability a contract should trade at according to your causal tree model. The difference between thesis-implied price and market price is your edge.

Node Probability

Node probability is the estimated likelihood assigned to a specific node in a causal tree. Changes to leaf node probabilities cascade upward through the tree, updating the root probability and all derived edge calculations.

Edge Detection

Edge detection is the systematic process of scanning prediction markets to find contracts where the market price diverges from your thesis-implied fair value by more than the cost of execution.

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