Create
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.
||
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
Related Prompts
Related Guides
- How to Use AI to Write a MEL Plan
A MEL plan is the most requested M&E deliverable, and the most time-consuming to write from scratch. AI can draft the structure, populate indicator tables, and flag gaps, but only if you guide it section by section.
- How to Use AI to Design an M&E Framework
Logframe, results framework, outcome mapping, or something else? AI can help you choose the right structure and populate it, but you need to tell it what decisions the framework must support.