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Causal decomposition is what makes Rekko different from a simple odds aggregator. Instead of just giving you a number, Rekko explains why it estimates a given probability by breaking it into weighted causal factors with Bayesian updates.

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What you get

Request ?expand=causal on any analysis or signal endpoint: 
{
  "causal": {
    "overall_probability": 0.74,
    "overall_confidence": 0.73,
    "method": "weighted_bayesian",
    "factors": [
      {
        "claim": "Recent inflation data supports a rate cut",
        "direction": "supports_yes",
        "weight": 0.35,
        "confidence": 0.82,
        "prior": 0.50,
        "posterior": 0.78,
        "evidence": [
          "CPI fell to 2.1% in February, below 2.3% forecast",
          "Core PCE at 2.4%, trending toward 2% target"
        ]
      },
      {
        "claim": "Labor market remains tight",
        "direction": "supports_no",
        "weight": 0.25,
        "confidence": 0.71,
        "prior": 0.50,
        "posterior": 0.35,
        "evidence": [
          "Unemployment at 3.7%, near historic lows",
          "Wage growth at 4.1% YoY"
        ]
      },
      {
        "claim": "Fed forward guidance signals openness to cuts",
        "direction": "supports_yes",
        "weight": 0.40,
        "confidence": 0.68,
        "prior": 0.50,
        "posterior": 0.82,
        "evidence": [
          "Powell's February testimony noted 'progress on inflation'",
          "Dot plot median shifted down by 25bps"
        ]
      }
    ],
    "generated_at": "2026-03-24T14:30:00Z"
  }
}

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Factor anatomy

Each CausalFactor represents one driver of the outcome:
FieldTypeDescription
claimstringWhat this factor asserts
directionstring"supports_yes", "supports_no", or "neutral"
weightfloat (0-1)Relative importance — weights across factors sum to ~1.0
confidencefloat (0-1)How confident Rekko is in this specific factor
priorfloat (0-1)Base rate probability before evidence
posteriorfloat (0-1)Updated probability after incorporating evidence
evidencestring[]Key evidence supporting this factor

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How it works

  1. Factor identification — The research pipeline identifies 3-7 independent causal drivers of the outcome
  2. Prior estimation — Each factor starts with a base rate (prior) drawn from historical patterns
  3. Evidence gathering — Deep web research collects relevant evidence for each factor
  4. Bayesian update — Each factor’s prior is updated to a posterior based on the evidence strength
  5. Weighted aggregation — Factors are combined using their weights to produce the overall probability
The method field indicates the aggregation approach:
MethodDescription
weighted_bayesianStandard weighted Bayesian combination (default)
linearSimple weighted average of posteriors
log_oddsLog-odds weighted combination

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Why this matters

Most prediction market APIs just give you a price. Rekko gives you the reasoning chain:
  • Audit the logic — See exactly which factors drive the estimate and whether you agree
  • Identify blind spots — Check if important factors are missing from the decomposition
  • Track changes — When you re-analyze later, see which factors shifted and why
  • Override confidently — If you have domain expertise on a specific factor, you can judge whether the overall estimate is too high or too low

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Requesting causal decomposition

Add ?expand=causal to any analysis or signal endpoint: 
# On an analysis
curl "https://api.rekko.ai/v1/markets/kalshi/KXFED-26MAR19/analysis?expand=causal" \
  -H "Authorization: Bearer YOUR_API_KEY"

# On a signal
curl -X POST https://api.rekko.ai/v1/signals \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"platform": "kalshi", "market_id": "KXFED-26MAR19"}' \
  | jq '.causal.factors[] | {claim, direction, weight, posterior}'