Research and Data Analysis

Propensity Score Matching for Quasi-Experimental Designs Training Course

Introduction

In today’s data-driven research landscape, ensuring accurate causal inference in non-randomized studies is crucial. Propensity Score Matching (PSM) for Quasi-Experimental Designs Training Course is designed to equip researchers, data analysts, and policy evaluators with advanced skills to address selection bias and improve the credibility of their results. With the rise in demand for causal inference, program evaluation, and impact assessment in health, education, social science, and economics, mastering PSM techniques has become a strategic necessity for researchers and analysts. This course combines theoretical depth with practical application, focusing on real-world case studies and hands-on simulations using statistical software.

This course offers a robust understanding of quasi-experimental design, matching algorithms, treatment effect estimation, and sensitivity analysis. Through carefully curated modules, participants will explore how PSM reduces bias in observational studies, ensuring valid and reliable outcomes. Participants will gain fluency in applying PSM using tools like R, STATA, or SPSS, while also learning to critically interpret and present their findings.

Programme Curriculum

Propensity Score Matching for Quasi-Experimental Designs Training Course

Introduction

Course Objectives

Understand the fundamentals of quasi-experimental designs in non-randomized studies.
Explore the theoretical framework behind propensity score matching.
Identify the key assumptions for causal inference using PSM.
Learn to estimate propensity scores using logistic regression.
Apply nearest neighbor, caliper, and kernel matching algorithms.
Conduct balance diagnostics to assess matching quality.
Evaluate average treatment effects using matched samples.
Perform sensitivity analysis for hidden bias detection.
Use R, STATA, or SPSS for PSM implementation.
Integrate PSM in program evaluation and impact studies.
Interpret PSM results for evidence-based policy recommendations.
Build publishable research outputs using matched observational data.
Critically appraise PSM applications in peer-reviewed literature.

Target Audiences

Public health researchers
Educational evaluators
Government policy analysts
Data scientists in social sciences
Economists conducting impact assessments
Graduate students in quantitative fields
NGO monitoring and evaluation officers
Healthcare outcomes researchers

Course Duration: 5 days

Course Modules

Module 1: Introduction to Quasi-Experimental Designs

Definition and importance of quasi-experiments
Comparison with randomized controlled trials
Selection bias in observational studies
Role of PSM in quasi-experiments
Overview of real-world applications
Case Study: Evaluating an education reform program

Module 2: Understanding Propensity Scores

Concept and mathematical foundation
Confounders and covariates
Logistic regression for score estimation
Common pitfalls in score estimation
Overlap and common support regions
Case Study: Healthcare access in rural populations

Module 3: Matching Techniques and Algorithms

Nearest neighbor matching
Caliper matching
Radius and kernel matching
Matching with/without replacement
Visualizing matched pairs
Case Study: Employment outcomes in microfinance recipients

Module 4: Balance Diagnostics and Quality Checks

Covariate balance before and after matching
Standardized mean differences
Visual diagnostic plots (Love plots)
Statistical tests for balance
Iterating matching until balance
Case Study: Tobacco control policy evaluation

Module 5: Estimating Treatment Effects

Average Treatment Effect (ATE)
Average Treatment Effect on Treated (ATT)
Unmatched vs. matched estimation
Confidence intervals and statistical inference
Adjusting standard errors
Case Study: Impact of mentoring on student success

Module 6: Sensitivity and Robustness Analysis

Unobserved confounders
Rosenbaum bounds
E-value computation
Stratification sensitivity checks
Interpreting robustness results
Case Study: Agricultural subsidies and crop yield

Module 7: Software Implementation (R, STATA, SPSS)

Installing and configuring packages
Code walkthrough for PSM
Running diagnostics and treatment effect estimation
Exporting results and plots
Troubleshooting common errors
Case Study: Comparing R and STATA outputs for the same dataset

Module 8: Real-World Applications and Reporting

Reporting guidelines for matched studies
Visualizing impact using graphs and tables
Ethical considerations and limitations
Integrating findings in reports and publications
Writing PSM results for grant proposals
Case Study: Public health campaign effectiveness report

Training Methodology

Interactive live webinars and Q&A sessions
Guided hands-on exercises with real datasets
Downloadable scripts and step-by-step tutorials
Group-based activities and problem-solving tasks
Continuous assessment through mini-projects
Final evaluation with project-based report presentation

Register as a group from 3 participants for a Discount

Send us an email: info@fineskilltrainingcenter.org or call +254769199797

Certification

Upon successful completion of this training, participants will be issued with a globally- recognized certificate.

Tailor-Made Course

We also offer tailor-made courses based on your needs.

Key Notes

a. The participant must be conversant with English.

b. Upon completion of training the participant will be issued with an Authorized Training Certificate

c. Course duration is flexible and the contents can be modified to fit any number of days.

d. The course fee includes facilitation training materials, 2 coffee breaks, buffet lunch and A Certificate upon successful completion of Training.

e. One-year post-training support Consultation and Coaching provided after the course.

f. Payment should be done at least a week before commence of the training, to Fineskill Training Center account, as indicated in the invoice so as to enable us prepare better for you.

Skills & Software Covered

Propensity scoring quasi-experiments causal inference

Programme Fee

Duration: 5 Days

Method: Online or Physical

Online Price: $1,000

Physical Price: $1,500

Certification: Accredited Certificate

View Available Dates 📄 Download Course Outline (PDF)

Available Sessions

Location

Month

Year

Oct 19 2026

19 Oct — 23 Oct 2026

💻 Online / Virtual session

Online $1,000