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Create a Process Tracing Protocol

Create a process tracing protocol for causal inference in single-case or small-n evaluations, with hypothesis formulation, evidence tests, and Bayesian confidence updating.

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You are a senior MEAL specialist with expertise in qualitative causal inference methods. Your task is to create a process tracing protocol for evaluating whether and how a program caused an observed outcome. The evaluation involves a single case or small number of cases where statistical comparison is not feasible. Process tracing is appropriate because the evaluation needs to establish whether the program was a necessary or sufficient cause of the observed change. **Develop the following components:** 1. **Causal Hypothesis Formulation:** * Primary hypothesis: The specific causal claim to be tested * Alternative hypotheses (at least 3): Other plausible causal explanations * Null hypothesis: The outcome would have occurred without the program * For each hypothesis, specify the causal mechanism (the step-by-step process through which the cause is expected to produce the effect) 2. **Causal Mechanism Mapping:** * Break down the primary hypothesis into a sequence of 4-7 causal steps * For each step in the mechanism: - What entity is involved - What action or transmission occurs - What observable evidence would confirm this step occurred - What the absence of evidence would mean * Create a mechanism diagram showing the causal chain 3. **Evidence Tests Design:** For each key step in the causal mechanism, design diagnostic tests using Beach and Pedersen's four test types: * **Straw-in-the-wind tests:** Evidence that is consistent with the hypothesis but not confirmatory (neither necessary nor sufficient) * **Hoop tests:** Evidence that must be present for the hypothesis to survive (necessary but not sufficient) * **Smoking gun tests:** Evidence that strongly confirms the hypothesis if found (sufficient but not necessary) * **Doubly decisive tests:** Evidence that both confirms the hypothesis and eliminates alternatives (both necessary and sufficient) * For each test: describe the specific evidence sought, classify the test type, specify the source, and state what passing or failing the test means for the hypothesis 4. **Evidence Collection Plan:** * Evidence inventory: What types of evidence are needed (documents, testimony, observational, physical) * Source mapping: Who has this evidence, where it is located, how to access it * Prioritization: Which evidence tests are most diagnostic and should be pursued first * Timeline and sequencing of evidence collection * At least 10 specific pieces of evidence to seek, with their test classification 5. **Confidence Updating Framework:** * Prior confidence levels for each hypothesis (before evidence collection) * Bayesian-inspired updating approach: How each piece of evidence shifts confidence toward or away from each hypothesis * Confidence scale (e.g., very low, low, moderate, high, very high) with threshold definitions * Evidence tracking matrix with columns: Evidence Item, Test Type, Result (passed/failed/inconclusive), Confidence Shift (direction and magnitude), Updated Confidence Level 6. **Alternative Explanation Assessment:** * For each alternative hypothesis, specify the evidence tests that would confirm or eliminate it * Interaction effects: Can the program and alternative causes both be contributing causes? How to assess their relative contributions * Equifinality consideration: Could multiple causal paths lead to the same outcome? 7. **Reporting Template:** * Narrative causal account structure * Evidence summary table * Final confidence assessment for each hypothesis * Limitations and caveats * "Weight of evidence" conclusion format **Output Format:** Deliver all components as clearly labeled sections. Evidence tests should be formatted as a detailed table. The causal mechanism should be presented as both a narrative sequence and a visual diagram description. The confidence updating framework should include a worked example showing how evidence shifts confidence.
process-tracingcausal-inferencebeach-pedersenbayesian-updatingsingle-caseevidence-testsqualitative-methods