Training on Experimental and Quasi-Experimental Design (RCTs)

A program launches. Funding is spent. Workshops are delivered. Beneficiaries are reached.

A year later, the report proudly announces:

"Outcomes improved significantly."

Excellent.

But then comes the uncomfortable question:

"Compared to what?"

Silence.

Maybe incomes increased because of the program. Maybe the economy improved. Maybe weather conditions were favorable. Maybe participants were already more motivated than everyone else.

Without a credible comparison, it's surprisingly easy to mistake coincidence for impact.

That's why the most important question in evaluation isn't:

"What happened?"

It's:

"What would have happened if we had done nothing?"

In this course, you'll learn how to:

• Design rigorous impact evaluations using experimental and quasi-experimental methods
• Establish credible causal relationships between interventions and outcomes
• Apply Randomized Controlled Trials (RCTs) and alternative evaluation approaches effectively
• Minimize bias and strengthen the credibility of findings
• Generate evidence that policymakers, donors, governments, and stakeholders trust

And yes, we'll discuss why some programs receive credit for outcomes they never actually caused.

Overview

Governments, development organizations, NGOs, research institutions, healthcare systems, educational organizations, and private-sector entities increasingly require robust evidence to determine whether policies, programs, interventions, and investments are achieving intended results. Decision-makers are no longer satisfied with measuring activities and outputs alone; they need credible evidence of impact.

Experimental and quasi-experimental research designs provide some of the most powerful methodologies available for evaluating causal effects. These approaches allow researchers and evaluators to estimate what would have happened in the absence of an intervention by constructing valid comparison groups and counterfactual scenarios.

Randomized Controlled Trials (RCTs) are widely regarded as the gold standard for causal inference because random assignment minimizes selection bias and strengthens internal validity. However, practical, ethical, operational, political, and financial constraints often make randomization difficult or impossible. In such situations, quasi-experimental methods offer robust alternatives for estimating causal effects using observational data.

Modern evaluation practice increasingly combines rigorous impact evaluation methods with advances in econometrics, behavioral science, machine learning, implementation research, and policy analysis to generate actionable evidence for decision-making.

This course equips participants with practical and strategic expertise in designing, implementing, analyzing, and interpreting experimental and quasi-experimental evaluations. Participants will learn how to select appropriate designs, assess validity, manage implementation challenges, analyze data, and communicate findings effectively.

Through practical exercises, evaluation simulations, case studies, data analysis workshops, and real-world applications, participants will develop the skills needed to conduct high-quality impact evaluations across diverse sectors and contexts.

Duration

10 Days

Who Should Attend

• Monitoring and Evaluation (M&E) professionals
• Policy analysts
• Economists
• Researchers and academics
• Development practitioners
• Program managers
• Government evaluation officers
• Public health researchers
• Social scientists
• Impact assessment specialists
• Data analysts and statisticians
• Donor and development agency staff

Individual Impact

• Strengthen impact evaluation expertise
• Improve causal analysis capabilities
• Enhance research design competencies
• Develop advanced evaluation and analytical skills
• Improve evidence interpretation and communication
• Increase confidence in designing and managing evaluations

Organizational Impact

• Improve quality and credibility of evaluations
• Strengthen accountability and transparency
• Support evidence-based decision-making
• Improve resource allocation effectiveness
• Enhance program learning and adaptation
• Increase confidence among donors and stakeholders
• Strengthen organizational evaluation systems

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

• Understand the foundations of causal inference
• Design rigorous experimental and quasi-experimental studies
• Develop valid counterfactual frameworks
• Implement Randomized Controlled Trials effectively
• Apply quasi-experimental evaluation methods
• Assess internal and external validity
• Analyze and interpret impact evaluation results
• Address ethical and operational evaluation challenges
• Communicate causal evidence to stakeholders
• Support evidence-based policy and program decisions

Module 1: Foundations of Experimental and Quasi-Experimental Design

• Understanding causality and impact evaluation
• Correlation versus causation
• Counterfactual thinking
• Sources of bias in evaluation
• Internal and external validity
• Exercise: Defining causal evaluation questions
• Case Study: Evaluation failures caused by weak design

Module 2: Randomized Controlled Trials (RCTs)

• Principles of randomization
• Treatment and control groups
• Random assignment techniques
• Experimental protocols and implementation
• Advantages and limitations of RCTs
• Practical: Designing an RCT
• Case Study: Landmark development and public health RCTs

Module 3: Sampling, Power Analysis, and Data Collection

• Sampling strategies for impact evaluations
• Statistical power and sample size calculations
• Baseline and endline surveys
• Data quality assurance
• Attrition management
• Exercise: Evaluation sampling design
• Case Study: Large-scale impact evaluation planning

Module 4: Ethical and Operational Considerations

• Research ethics in experimental evaluations
• Informed consent and participant protection
• Political and organizational challenges
• Managing implementation risks
• Stakeholder engagement strategies
• Practical: Ethical review simulation
• Case Study: Ethical dilemmas in field experiments

Module 5: Difference-in-Differences (DiD)

• Foundations of quasi-experimental methods
• Before-and-after comparisons
• Parallel trends assumption
• Estimation and interpretation
• Policy evaluation applications
• Exercise: Difference-in-differences analysis
• Case Study: Evaluating public sector reforms

Module 6: Regression Discontinuity Designs (RDD)

• Cutoff-based interventions
• Sharp and fuzzy RDD approaches
• Estimation techniques
• Diagnostic testing
• Interpretation of treatment effects
• Practical: RDD implementation workshop
• Case Study: Education and social policy evaluations

Module 7: Matching and Propensity Score Methods

• Addressing selection bias
• Matching methodologies
• Propensity score estimation
• Balance testing and diagnostics
• Strengths and limitations
• Exercise: Propensity score matching analysis
• Case Study: Evaluating social programs without randomization

Module 8: Instrumental Variables and Natural Experiments

• Understanding endogeneity
• Instrumental variable estimation
• Natural experiment approaches
• Identification strategies
• Interpreting causal estimates
• Practical: Instrumental variable analysis
• Case Study: Labor, health, and education evaluations

Module 9: Data Analysis, Robustness, and Evidence Quality

• Impact estimation techniques
• Robustness and sensitivity analysis
• Threats to validity
• Interpreting uncertainty and confidence intervals
• Evidence grading frameworks
• Exercise: Evaluation quality assessment
• Case Study: Contradictory evaluation findings

Module 10: Communicating Impact and Evaluation Results

• Reporting causal findings effectively
• Visualization of impact estimates
• Communicating with policymakers and donors
• Evidence utilization strategies
• Emerging trends in impact evaluation
• Capstone Exercise: Complete Experimental or Quasi-Experimental Evaluation Design
• Case Study: Evidence influencing major policy decisions