method-transfer-engine

Method Transfer Engine

Rigorous framework for adapting statistical methods across domains and settings

Use this skill when: adapting a method from one field to another, extending a method to a new setting, formalizing an intuitive connection between methods, or verifying that a transferred method retains its properties.

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The Transfer Framework

What is Method Transfer?

Taking a technique that works in Setting A and adapting it to work in Setting B, while:

• Preserving desirable theoretical properties
• Identifying what changes are needed
• Understanding what can and cannot transfer

Transfer Quality Spectrum

Direct Application → Minor Adaptation → Major Modification → Inspired-By
      │                    │                   │                  │
   Same theory         Adjust for          Rewrite theory      New method,
   applies            new setting          for new setting     similar spirit

Transfer Success Criteria

A successful transfer must:

1. Solve the target problem - Method actually helps in new setting
2. Preserve key properties - Consistency, efficiency, robustness transfer
3. Have clear assumptions - Know what's required in new setting
4. Be verifiable - Can prove/simulate that it works
5. Add value - Better than existing approaches

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The 6-Phase Protocol

This protocol provides a systematic approach to method transfer, covering all critical steps from source extraction through validation.

Source Extraction

Goal: Extract the core mathematical and algorithmic essence of the source method

# Template for source method extraction
extract_source_method <- function(method_name, reference) {
  list(
    name = method_name,
    estimand = "formal expression of what is estimated",
    estimator = "formula for the estimator",
    assumptions = c("A1: condition", "A2: condition"),
    properties = c("consistency", "asymptotic normality"),
    algorithm = c("Step 1: ...", "Step 2: ..."),
    complexity = "O(n^2) or similar"
  )
}

# Example: Extract Lasso from signal processing
lasso_extraction <- list(
  name = "Lasso/Basis Pursuit",
  field = "Signal Processing / Compressed Sensing",
  estimand = "argmin ||y - Xb||_2^2 + lambda * ||b||_1",
  key_insight = "L1 penalty induces sparsity via soft thresholding",
  assumptions = c("RIP condition", "Incoherence"),
  properties = c("Sparse solution", "Variable selection consistency")
)