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Foundations of Policy Evaluation

Introduction to Policy Evaluation and Impact Assessment

The Counterfactual and the Fundamental Evaluation Problem

Key Measures of Program Impact

Randomized Controlled Trials

Structure and Purpose of Randomized Controlled Trials

Designing an RCT for a Public Program

Threats and Practical Considerations in RCTs

Quasi-Experimental Designs

When Randomization Is Not Feasible

Overview of Common Quasi-Experimental Approaches

Assessing Credibility in Quasi-Experimental Studies

Difference-in-Differences Analysis

Difference-in-Differences Logic and Setup

The Parallel Trends Assumption

Interpreting Difference-in-Differences Results

Applying Evidence to Policy Decisions

Selecting an Evaluation Design

Communicating Findings and Limitations

Quiz

Question 1: Which statement best describes a counterfactual in policy evaluation?

Question 2: What is the primary purpose of random assignment in a randomized controlled trial?

Question 3: When is a quasi-experimental design most appropriate?

Question 4: In a difference-in-differences analysis, what does the parallel trends assumption require?

Question 5: A program's treatment group improves more than its comparison group after implementation. What additional analysis is needed before concluding that the program caused the improvement?

Survey and Feedback

Course Feedback Survey

Are you ready to measure the true impact of public policies and programs. Welcome to "Foundations of Policy Evaluation and Quantitative Impact Assessment," a comprehensive course designed to equip you with the analytical tools needed to determine what works, what doesn't, and why.

Are you ready to measure the true impact of public policies and programs? Welcome to "Foundations of Policy Evaluation and Quantitative Impact Assessment," a comprehensive course designed to equip you with the analytical tools needed to determine what works, what doesn't, and why. In today's data-driven world, simply observing a change after a policy is implemented is not enough to prove success. You need rigorous methods to establish causal relationships and avoid common pitfalls like selection bias. This course dives deep into the core principles of causal inference, starting with the fundamental evaluation problem and the concept of the counterfactual. You will explore the gold standard of Randomized Controlled Trials (RCTs), learning how to design them, understand their limitations, and apply them to real-world public programs. When randomization isn't feasible, you'll master Quasi-Experimental Designs, including matching, regression discontinuity, and interrupted time series. Furthermore, we dedicate an entire section to Difference-in-Differences analysis, teaching you how to leverage parallel trends to extract meaningful insights. Finally, you will learn how to translate complex data into actionable policy decisions, effectively communicating your findings and limitations to non-technical stakeholders. Whether you are a policy analyst, researcher, public administrator, or student, this course will transform how you evaluate program effectiveness and empower you to make evidence-based decisions that drive real societal change. Enroll today and take the first step toward mastering quantitative impact assessment!

Foundations of Policy Evaluation and Quantitative Impact Assessment

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            <p>The rigorous evaluation of public programs is essential for determining their true effectiveness and guiding evidence-based decision-making. This course, Foundations of Policy Evaluation and Quantitative Impact Assessment, provides a comprehensive examination of the methodologies used to measure the causal impact of policy interventions. It addresses the fundamental problem of causal inference, emphasizing the critical role of the counterfactual in distinguishing between mere correlation and actual causation. By exploring key measures of program impact and the distinction among program inputs, activities, outputs, and outcomes, learners will develop a robust framework for formulating precise evaluation questions and understanding why simply observing a change after a program's implementation is insufficient to prove its efficacy.</p>

<p>The curriculum systematically progresses through the primary quantitative designs utilized in modern policy analysis. It begins with an in-depth exploration of Randomized Controlled Trials (RCTs), covering their structural advantages, design principles, and potential threats to internal and external validity. Recognizing that randomization is frequently constrained by ethical, political, or practical limitations, the course subsequently examines quasi-experimental approaches. A dedicated focus is placed on Difference-in-Differences analysis, detailing its foundational logic, the necessity of the parallel trends assumption, and the accurate interpretation of its results. Ultimately, the course bridges the gap between statistical analysis and practical application, equipping learners with the skills to select appropriate evaluation designs, assess the credibility of empirical evidence, and effectively communicate complex findings and their limitations to stakeholders and policymakers.</p>
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