Difference-in-Differences Impact Evaluation Training Course

Course overview

Policy teams, finance functions, regulators, and commercial analysts are often asked whether a programme, pricing change, subsidy, investment, or operational intervention caused an observed result. Simple before-and-after comparisons cannot separate the intervention effect from wider market movements, seasonality, inflation, or pre-existing differences between groups. Difference-in-Differences (DiD) provides a disciplined way to estimate causal impact when randomised experiments are not feasible, but only when the comparison group, treatment timing, assumptions, and inference are handled correctly.

This five-day course teaches participants to design, estimate, test, and communicate DiD studies for real business, public-policy, and financial decisions. Participants build two-group/two-period models, regression-based DiD specifications, event studies, and staggered-adoption models. They learn to assess the parallel-trends assumption, select credible control groups, interpret interaction coefficients, handle clustered standard errors, investigate anticipation and spillover effects, and avoid common errors associated with two-way fixed-effects estimators.

Instruction combines econometric explanation with guided analysis in Stata, R, Python, and Excel. Cases include programme evaluation, branch-level commercial interventions, tax or regulatory changes, and investment incentives. Each participant develops an impact-evaluation design for a workplace-relevant intervention and produces a reproducible DiD analysis pack: a research question, treatment and comparison-group definition, data specification, model plan, diagnostic charts, results table, and concise decision briefing.

The course is suited to professionals who already work with operational, financial, customer, labour-market, or policy data and need to make defensible causal claims rather than report descriptive correlations. Managers gain staff who can commission, challenge, and use impact evaluations with clearer evidence standards.

Course objectives

By the end of this course, participants will be able to:

• Formulate a Difference-in-Differences research question with defined treatment, outcome, unit of analysis, and observation periods
• Construct treatment and comparison groups using eligibility rules, baseline characteristics, and exposure timing
• Estimate a two-group/two-period DiD model using an interaction-term regression specification
• Test the parallel-trends assumption using pre-treatment plots, placebo tests, and event-study coefficients
• Apply fixed effects and clustered standard errors appropriate to panel and repeated cross-sectional data
• Diagnose staggered-treatment bias and select cohort-aware DiD estimators for multiple adoption periods
• Interpret DiD coefficients as business or policy effects with confidence intervals, units, and practical significance
• Produce a reproducible impact-evaluation pack containing code, diagnostics, model outputs, and a decision briefing

Benefits of attending

Target competencies

Who should attend

• Economic Analysts — who must estimate the effects of policies, incentives, and market interventions
• Financial Planning and Analysis Managers — who need credible evidence on the returns from business initiatives
• Policy Analysts — who evaluate public programmes where randomised trials are unavailable
• Data Analysts — who convert operational and customer data into causal performance evidence
• Risk and Regulatory Analysts — who assess the effects of rule changes, compliance programmes, and controls
• Monitoring and Evaluation Specialists — who need stronger quasi-experimental designs for programme reporting

Requirements and prerequisites

Participants should be comfortable reading tables and charts, working with structured data in spreadsheets or a statistical package, and interpreting basic regression output such as coefficients, standard errors, and confidence intervals. Prior exposure to ordinary least squares regression, panel data concepts, and hypothesis testing is helpful; participants should understand the difference between correlation and causation. Bring a laptop with Excel and either Stata, R/RStudio, or Python available if possible. Advanced econometric theory, calculus, machine learning, randomised-trial experience, and prior Difference-in-Differences work are not required.

Training methodology

The instructor introduces each DiD concept through a short technical briefing, then demonstrates its implementation using an annotated dataset in Stata, R, Python, and Excel. Participants work through individual coding and interpretation exercises before comparing modelling choices in small groups. Cases require participants to identify treated and untreated units, plot pre-trends, estimate models, and challenge assumptions. Daily debriefs focus on what a decision-maker can and cannot conclude. On day five, participants apply the method to a workplace intervention and refine an evaluation plan with instructor feedback.

Course outline

Day 1: Designing a credible Difference-in-Differences study

• Causal estimands, counterfactuals, and the DiD identification logic
• Two-group/two-period treatment and control design
• Defining intervention dates, exposure rules, and outcome measures
• Selecting comparison groups from operational or administrative data
• Panel data versus repeated cross-sectional data structures
• Data quality checks for identifiers, dates, missing values, and treatment coding
• Visualising group means before and after an intervention

Workshop: Participants scope a branch-performance intervention case and produce a treatment-control diagram, outcome definition, and initial data-quality checklist.

Day 2: Estimating core DiD models

• The DiD interaction term and its regression interpretation
• Manual DiD calculations in Excel pivot tables
• Estimating ordinary least squares DiD models in Stata, R, and Python
• Unit fixed effects and time fixed effects
• Control variables and their role in DiD specifications
• Coefficient tables, confidence intervals, and marginal effect interpretation
• Clustered standard errors at the treatment assignment level

Workshop: Participants estimate and interpret a core DiD model for a pricing-policy case, producing a regression table and management-ready effect statement.

Day 3: Testing assumptions and strengthening inference

• Parallel-trends assumption and its substantive meaning
• Pre-treatment trend charts and group-level descriptive diagnostics
• Event-study regressions with lead and lag indicators
• Placebo timing tests and placebo outcome tests
• Anticipation effects, spillovers, and contamination of control units
• Serial correlation and why conventional standard errors can mislead
• Sensitivity analysis for alternative samples, windows, and specifications

Workshop: Participants build an event-study chart and a placebo test for a subsidy case, then write an assumptions-and-limitations note.

Day 4: Staggered adoption and advanced DiD applications

• Multiple treatment cohorts and staggered implementation schedules
• Limitations of conventional two-way fixed-effects estimators
• Negative weighting and contaminated comparisons in staggered DiD
• Cohort-specific average treatment effects
• Callaway-Sant'Anna and Sun-Abraham estimator approaches
• Dynamic treatment effects and post-treatment event-time windows
• Heterogeneous effects by customer segment, region, or firm size

Workshop: Participants analyse a phased rollout case using cohort-aware estimation and produce a comparison of conventional and staggered-adoption results.

Day 5: Communicating results and applying DiD at work

• Translating statistical effects into financial, operational, or policy metrics
• Distinguishing statistical significance from decision significance
• Results-table design for executive and technical audiences
• Writing transparent assumptions, exclusions, and limitations
• Reproducible code, data dictionaries, and model documentation
• Peer review checklist for internal impact evaluations
• Planning a DiD study for a live organisational intervention

Workshop: Participants complete a workplace DiD analysis pack and present a five-minute recommendation covering design, diagnostics, estimated impact, and decision implications.

Tools & standards covered

Stata, RStudio, Python, Microsoft Excel

A typical training day

Live online deliveries follow the same structure in the East Africa Time zone, with shorter screen blocks and longer breaks.

What the fee includes

How you can take this course

Certification

Participants who complete the full five days receive the Skillset Development Certificate of Completion, stating the course title, course code, dates and delivery format — suitable for professional-development records and employer reimbursement.

Frequently asked questions