Description

This curriculum spans the technical, organizational, and ethical dimensions of program evaluation at a scale comparable to multi-workshop internal capability programs in large enterprises, addressing the coordination of data infrastructure, causal analysis, compliance, and cross-functional decision-making required to operationalize data-driven evaluation across complex organizations.

Module 1: Defining Evaluation Objectives and Stakeholder Alignment

Select appropriate evaluation goals based on organizational KPIs, balancing short-term operational needs with long-term strategic outcomes.
Map decision rights across departments to identify who controls data access, model deployment, and budget allocation for evaluation activities.
Negotiate evaluation scope with legal and compliance teams when program outcomes impact regulated domains such as healthcare or finance.
Document assumptions about causality when stakeholders expect attribution of business results to specific data interventions.
Establish escalation paths for resolving conflicts between business units on what constitutes a “successful” evaluation outcome.
Define thresholds for actionability in evaluation results, including minimum effect sizes and confidence levels required for decision-making.
Integrate equity considerations into evaluation design by identifying vulnerable subpopulations that may be disproportionately affected.

Module 2: Data Infrastructure Readiness Assessment

Audit lineage and provenance of input datasets to determine whether historical data supports valid pre-intervention baselines.
Evaluate the latency and reliability of data pipelines feeding evaluation systems, particularly when real-time decisions are involved.
Assess schema stability across source systems to determine feasibility of longitudinal tracking for outcome metrics.
Identify gaps in logging practices that prevent reconstruction of decision contexts for retrospective evaluation.
Configure data retention policies that balance evaluation needs with privacy regulations and storage costs.
Implement data versioning for training and evaluation datasets to ensure reproducibility of results over time.
Design fallback mechanisms for evaluation systems when primary data sources experience outages or schema changes.

Module 3: Causal Inference and Counterfactual Design

Select between randomized control trials and quasi-experimental methods based on operational feasibility and stakeholder tolerance for non-random assignment.
Adjust for selection bias in observational data by implementing propensity score matching or inverse probability weighting.
Determine appropriate time windows for pre- and post-intervention analysis to avoid contamination from external shocks.
Validate parallel trends assumption in difference-in-differences designs using historical data from pre-treatment periods.
Quantify uncertainty in causal estimates by conducting sensitivity analyses for unmeasured confounding variables.
Handle interference between treatment units when evaluating programs with network effects or spillover impacts.
Decide whether to use intent-to-treat or per-protocol analysis based on adherence rates and policy relevance.

Module 4: Metric Selection and Outcome Operationalization

Translate high-level business objectives into measurable indicators, resolving ambiguity in definitions such as “customer satisfaction” or “engagement.”
Weight composite metrics based on stakeholder priorities, documenting trade-offs between competing outcomes.
Implement guardrail metrics to detect unintended consequences, such as increased support tickets or decreased retention.
Address denominator ambiguity in rate-based metrics, particularly when user eligibility criteria change over time.
Standardize metric computation across teams to prevent conflicting reports from different analytical sources.
Design cohort definitions that align with business logic, such as onboarding date, subscription tier, or geographic region.
Validate metric robustness by testing sensitivity to edge cases, such as null values or extreme outliers.

Module 5: Model Evaluation in Production Systems

Monitor model drift by comparing current prediction distributions to baseline training data, triggering retraining when thresholds are exceeded.
Implement shadow mode deployment to compare new model outputs against production models without affecting live decisions.
Track feature availability and quality in production to diagnose performance degradation unrelated to model accuracy.
Design fallback policies for model serving when inference latency exceeds service-level objectives.
Conduct fairness audits across demographic groups using disaggregated performance metrics and statistical tests.
Balance precision and recall based on operational cost structures, such as false positives in fraud detection leading to customer friction.
Log decision rationales for high-stakes predictions to support auditability and regulatory compliance.

Module 6: A/B Testing at Scale

Configure randomization units that align with business logic, such as user, account, or session, considering potential contamination.
Adjust sample size calculations for clustering effects when randomization occurs at a group level rather than individual level.
Implement holdback groups to measure long-term effects after a feature has been rolled out to the majority of users.
Control for multiple comparisons when testing multiple variants or metrics to maintain family-wise error rates.
Handle dynamic traffic allocation by ensuring randomization remains unbiased during ramp-up periods.
Address novelty effects by analyzing time-series trends in user behavior post-exposure.
Design cross-experiment coordination systems to prevent interference between concurrent tests sharing user populations.

Module 7: Ethical and Regulatory Compliance in Evaluation

Conduct privacy impact assessments when evaluation involves processing personally identifiable information or sensitive attributes.
Implement data minimization in evaluation datasets by excluding fields not essential to the analysis.
Obtain informed consent for experimental treatments when required by jurisdiction or institutional review boards.
Document algorithmic decision logic to comply with right-to-explanation requirements under regulations like GDPR.
Establish data access controls to limit evaluation data to authorized personnel based on role and need-to-know.
Report evaluation results transparently, including limitations and sources of uncertainty, when communicating with external stakeholders.
Design opt-out mechanisms for users who do not wish to participate in data-driven experiments.

Module 8: Reporting, Visualization, and Decision Support

Structure dashboards to highlight statistical significance, effect size, and practical significance, not just point estimates.
Use confidence intervals instead of p-values in executive reports to improve interpretation of uncertainty.
Design visualization hierarchies that allow users to drill from summary results to cohort-level and individual-level data.
Prevent misinterpretation of time-series charts by clearly marking intervention points and adjustment periods.
Automate report generation with version-controlled code to ensure consistency across evaluation cycles.
Integrate qualitative feedback into evaluation reports to contextualize quantitative findings.
Implement access controls on reporting platforms to prevent unauthorized access to sensitive program results.

Module 9: Scaling Evaluation Practices Across Organizations

Standardize evaluation templates and code libraries to reduce duplication and ensure methodological consistency.
Establish centralized review boards to assess evaluation proposals for methodological rigor and resource feasibility.
Integrate evaluation pipelines into CI/CD workflows to automate testing and deployment of analytical code.
Train domain teams on self-service evaluation tools while maintaining oversight through data governance frameworks.
Allocate shared evaluation resources based on program risk, investment size, and potential impact.
Develop escalation protocols for when evaluation findings contradict operational assumptions or strategic direction.
Institutionalize post-mortems after major evaluations to capture lessons learned and update best practices.