Causal Inference in Program and Policy Evaluation Training Course

Introduction

Understanding the true impact of programs and policies is fundamental for effective resource allocation and sustainable development. The Causal Inference in Program and Policy Evaluation Training Course is specifically designed for M&E professionals, policy analysts, researchers, and data scientists who are committed to rigorously determining whether an intervention caused an observed outcome, rather than simply being correlated with it. This intensive program equips participants with the essential frameworks and cutting-edge methodologies needed to move beyond descriptive statistics and confidently attribute changes to specific programs or policies.

This comprehensive training delves into the core principles and advanced techniques of causal inference, covering both experimental and quasi-experimental designs, as well as methods for drawing causal conclusions from observational data. Attendees will gain hands-on experience in identifying and addressing common threats to causal validity, such as selection bias, confounding, and reverse causality. By mastering causal inference in program and policy evaluation, professionals will be empowered to design more robust studies, conduct more credible analyses, and provide the high-quality evidence essential for data-driven decision-making and accountability in diverse sectors.

Target Audience

• M&E Specialists and Coordinators
• Policy Analysts and Advisors
• Impact Evaluation Researchers
• Data Scientists and Statisticians
• Public Health and Development Professionals
• Government Officials involved in Policy Assessment
• Academics and Graduate Students in Social Sciences

Duration

5 Days

Course Objectives

1. Understand the fundamental problem of causal inference and the counterfactual framework.
2. Differentiate between correlation and causation in program and policy contexts.
3. Design and implement experimental evaluations (Randomized Controlled Trials - RCTs).
4. Apply various quasi-experimental methods to estimate causal effects when randomization is not feasible.
5. Identify and address key challenges to causal inference, including selection bias and confounding.
6. Utilize statistical software to implement causal inference techniques.
7. Interpret and critically appraise causal findings from evaluation studies.
8. Effectively communicate causal evidence to inform policy and program decisions.

Course Content

Module 1: Foundations of Causal Inference in Evaluation

• The importance of causality in M&E and policy
• The counterfactual framework and potential outcomes
• Defining Average Treatment Effect (ATE) and Average Treatment Effect on the Treated (ATT)
• The Fundamental Problem of Causal Inference
• Introduction to Directed Acyclic Graphs (DAGs) for visualizing causal assumptions

Module 2: Randomized Controlled Trials (RCTs) for Causal Inference

• Principles of random assignment and its role in establishing causality
• Designing and implementing RCTs in various settings (individual, cluster randomization)
• Advantages and limitations of RCTs in policy evaluation
• Sample size considerations and power analysis for RCTs
• Analysis of data from RCTs and estimating causal effects

Module 3: Quasi-Experimental Designs: Propensity Score Matching (PSM)

• Introduction to quasi-experimental designs when randomization is not possible
• The concept of propensity scores and balancing covariates
• Steps for implementing Propensity Score Matching (PSM)
• Assessing balance, common support, and sensitivity analysis in PSM
• Interpreting results and assumptions of PSM

Module 4: Quasi-Experimental Designs: Difference-in-Differences (DiD)

• Introduction to the Difference-in-Differences (DiD) method for panel data
• The parallel trends assumption and methods for checking its plausibility
• Designing DiD studies and data requirements
• Estimating treatment effects using DiD models
• Extensions and robustness checks for DiD analysis

Module 5: Regression Discontinuity Design (RDD)

• Principles of Regression Discontinuity Design (RDD) based on a cutoff
• Sharp vs. Fuzzy RDD and their applications in policy evaluation
• Data requirements and assumptions of RDD
• Graphical and regression-based RDD analysis
• Local Average Treatment Effects (LATE) and interpretation of RDD findings

Module 6: Instrumental Variables (IV) and Other Advanced Methods

• The concept of Instrumental Variables (IV) for unobserved confounding
• Assumptions of IV (relevance, exclusion restriction, monotonicity)
• Finding valid instruments in practice
• Introduction to other advanced methods: Synthetic Control Method (SCM), Interrupted Time Series (ITS)
• Overview of when to apply different causal inference methods

Module 7: Addressing Selection Bias and Confounding

• Understanding sources of bias in observational studies
• The role of confounding variables and how to control for them
• Collider bias and selection bias in DAGs
• Strategies for covariate selection and adjustment
• Sensitivity analysis to unobserved confounders

Module 8: Introduction to Panel Data Methods for Causal Inference

• Benefits of panel data for causal inference (controlling for unobserved heterogeneity)
• Fixed Effects (FE) models for within-unit analysis
• Random Effects (RE) models and their assumptions
• The Hausman test for choosing between FE and RE models
• Applying panel data methods to program evaluation

Module 9: Causal Inference with Observational Data

• Challenges of establishing causality from purely observational data
• Strengthening causal claims in observational studies (e.g., strong theory, multiple methods)
• Introduction to matching and weighting methods beyond PSM (e.g., inverse probability weighting)
• Using Bayesian methods for causal inference
• Ethical considerations in observational causal studies

Module 10: Generalizability and External Validity

• Understanding the concept of external validity in causal inference
• Factors affecting the generalizability of causal findings
• Transportability of causal effects across different contexts
• Meta-analysis for synthesizing causal evidence
• Using theory and context to inform generalizability

Module 11: Interpreting and Communicating Causal Findings

• Translating complex causal estimates into actionable policy insights
• Presenting causal results clearly and transparently
• Using confidence intervals and statistical significance appropriately
• Communicating assumptions and limitations of causal studies
• Tailoring causal evidence communication for different audiences

Module 12: Ethical Considerations and Software Application for Causal Inference

• Ethical implications of causal inference studies (e.g., withholding treatment in RCTs)
• Data privacy and security in causal analysis
• Practical sessions: Implementing causal inference methods using statistical software (e.g., R, Stata, Python)
• Case studies of successful and challenging causal evaluations
• Latest trends and future directions in causal inference for M&E.

General remarks

• Customizable courses are available to address the specific needs of your organization.
• The participant must be conversant in English
• Participants who successfully complete this course will receive a certificate of completion from Lenol Development Center.
• The course fee for onsite training includes facilitation training materials, tea break and lunch.
• Accommodation and airport pick up are made upon request
• For any inquiries reach us through info@lenoldevelopmentcenter.com or +254 710 314 746
• Payment should be made to our bank