Introduction

In the age of evidence-based policymaking and data-driven decision-making, the ability to assess causal relationships is more crucial than ever. Causal Inference with Difference-in-Differences (DiD) Training Course is designed for researchers, analysts, economists, and professionals seeking to master one of the most widely-used quasi-experimental research designs. By leveraging panel data and pre/post-treatment comparisons, DiD enables robust evaluation of interventions when randomized controlled trials are not feasible.

This comprehensive, hands-on course equips participants with the theoretical foundations and applied skills needed to execute DiD analysis using modern statistical tools like R, Stata, and Python. Participants will explore real-world case studies across public policy, healthcare, economics, and education to solidify their understanding. Whether you're in academia, government, or the private sector, this training will give you the expertise to perform high-impact, credible research.

Programme Curriculum

Causal Inference with Difference-in-Differences Training Course

Introduction

Course Objectives

Understand the fundamentals of causal inference in observational studies
Define and explain the Difference-in-Differences (DiD) methodology
Distinguish between parallel trends and non-parallel trends assumptions
Apply DiD using statistical software (R, Stata, or Python)
Interpret DiD regression outputs correctly
Handle common threats to DiD validity
Conduct robustness checks and placebo tests
Evaluate heterogeneous treatment effects
Combine DiD with other designs (e.g., matching, synthetic control)
Understand staggered treatment adoption and event studies
Communicate findings through reproducible research reports
Analyze policy interventions using DiD frameworks
Critically assess published research using DiD methods

Target Audience

Policy Analysts
Economists and Econometricians
Public Health Researchers
Social Science Researchers
Government Officials and Statisticians
NGO Monitoring and Evaluation Teams
Graduate Students in Economics/Public Policy
Data Scientists and Quantitative Researchers

Course Duration: 5 days

Course Modules

Module 1: Foundations of Causal Inference

Introduction to causal inference and observational data
Types of research designs: experimental vs. quasi-experimental
Challenges in causal identification
Key assumptions in causal models
Overview of DiD as a strategy
Case Study: Evaluating minimum wage policy effects

Module 2: Introduction to Difference-in-Differences

Basic DiD setup and notations
Understanding counterfactuals
Pre-treatment and post-treatment comparisons
Interpreting simple DiD models
Visualization of trends
Case Study: Impact of a smoking ban on health outcomes

Module 3: Assumptions and Validity Checks

The parallel trends assumption
Testing for pre-treatment trends
Common pitfalls and how to avoid them
Graphical diagnostics
Alternative identification strategies
Case Study: Education reform and student performance

Module 4: Implementation in R, Stata, and Python

Setting up DiD in different platforms
Writing DiD code in R and Stata
Visualizing effects and trends
Running robustness checks
Automating result reports
Case Study: Economic stimulus and employment trends

Module 5: Extensions and Advanced Topics

Multiple time periods and staggered adoption
Event studies and treatment dynamics
DiD with matching or propensity scores
Adjusting for clustered errors
Dealing with heterogeneous treatment effects
Case Study: Infrastructure policy across regions

Module 6: Robustness and Sensitivity Analysis

Placebo tests and falsification checks
Alternative specifications
Subgroup analyses
Dealing with serial correlation
Statistical inference in DiD
Case Study: Crime rate and policing policies

Module 7: Communicating Results and Reporting

Writing policy-relevant summaries
Creating impact visualization dashboards
Reproducibility and code documentation
Ethics in reporting causal research
Publishing DiD studies
Case Study: COVID-19 lockdown policies and mental health

Module 8: Critically Reviewing DiD Literature

Framework for evaluating published DiD research
Common issues in empirical papers
Replicating published findings
Peer review and critique techniques
Applying learning to new datasets
Case Study: Analysis of universal basic income pilot studies

Training Methodology

Interactive lectures and demos
Hands-on coding sessions (R, Stata, Python)
Guided real-world data exercises
Peer group discussions
Capstone project with expert feedback
Certification on successful completion

Bottom of Form

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

Causal inference DiD methodology treatment effect analysis

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

Sep 21 2026

21 Sep — 25 Sep 2026

💻 Online / Virtual session

Online $1,000