Course Introduction
The Quasi-Experimental Research Design and Impact Analysis Training Course provides a practical and rigorous framework for evaluating interventions, programs, policies, and other real-world changes when randomized experiments are not feasible. Participants learn how quasi-experimental designs use naturally occurring variation, comparison groups, repeated observations, timing, eligibility rules, and other research structures to estimate intervention effects. The course connects research design principles with applied impact evaluation across development, public policy, education, health, business, and social research.
The training introduces the core logic of causal inference and explains the assumptions required to interpret observed differences as evidence about intervention effects. Participants examine treatment and comparison groups, potential outcomes, counterfactuals, treatment assignment mechanisms, confounding, selection bias, anticipation effects, spillovers, and interference. Particular attention is given to identifying the estimand of interest and ensuring that the research design, data structure, and analytical method are aligned with the causal question being investigated.
Participants develop practical expertise in major quasi-experimental approaches, including difference-in-differences, regression discontinuity designs, interrupted time series, matching, synthetic control methods, instrumental variables, and fixed-effects approaches. The course explores when these methods are appropriate, what assumptions support their interpretation, and how design-specific diagnostics can reveal weaknesses. Participants learn to distinguish the statistical association produced by a model from the causal interpretation that depends on substantive and methodological assumptions.
The course places strong emphasis on implementation and statistical analysis. Participants learn how to prepare longitudinal and panel datasets, construct treatment and comparison groups, specify regression models, investigate pre-treatment trends, test discontinuities, conduct placebo and falsification analyses, and estimate uncertainty appropriately. They also examine heterogeneous treatment effects, clustered observations, repeated interventions, staggered implementation, missing data, and treatment-effect variation across populations and time periods.
Advanced sessions address modern developments in quasi-experimental research, including staggered treatment adoption, event-study designs, synthetic difference-in-differences, generalized synthetic controls, heterogeneous treatment effects, sensitivity analysis, and causal machine learning. Participants also explore how simulation can be used to assess estimator behavior and how AI-assisted tools may support data preparation, coding, diagnostics, and research documentation. The course emphasizes that computational assistance does not remove the need for explicit identification assumptions, careful design assessment, and substantive knowledge.
By the end of the training, participants will be able to design and evaluate quasi-experimental studies, select appropriate identification strategies, conduct impact analyses, test important assumptions, and communicate findings with appropriate caution. Practical exercises reinforce the full analytical workflow from defining a causal question and constructing a counterfactual through estimation, robustness testing, interpretation, and reporting. Participants gain the ability to critically assess whether a quasi-experimental design supports the intended causal interpretation and to transparently communicate uncertainty and limitations.
Duration
5 days
Who Should Attend
-
Impact evaluation professionals designing or analyzing program and policy evaluations.
-
Researchers conducting causal studies where randomized controlled trials are not feasible or appropriate.
-
Economists and social scientists analyzing observational, longitudinal, or administrative datasets.
-
Monitoring and evaluation specialists assessing intervention effectiveness and program outcomes.
-
Policy researchers evaluating changes associated with government programs, regulations, or institutional reforms.
-
Public health researchers assessing interventions, service changes, health programs, and population-level initiatives.
-
Education researchers evaluating reforms, interventions, curricula, funding changes, and institutional programs.
-
Development practitioners conducting rigorous evaluations of projects and interventions.
-
Statisticians and data analysts seeking advanced causal inference and quasi-experimental methods.
-
Data scientists working with panel data, administrative data, natural experiments, and observational research.
-
Postgraduate students developing quantitative research involving intervention effects and causal analysis.
-
Research managers and analysts responsible for designing, implementing, or communicating impact evaluations.
Course Objectives
-
Explain the principles of causal inference, counterfactual reasoning, potential outcomes, treatment effects, and identification within quasi-experimental research.
-
Distinguish major quasi-experimental designs and determine their suitability based on treatment assignment, timing, data structure, comparison groups, and causal objectives.
-
Identify threats to causal interpretation, including confounding, selection bias, differential trends, anticipation, spillovers, attrition, and treatment contamination.
-
Design difference-in-differences analyses and evaluate the assumptions underlying credible comparisons of changes across treatment and comparison groups.
-
Apply regression discontinuity designs and assess continuity, bandwidth selection, manipulation, functional form, and local treatment-effect interpretation.
-
Develop interrupted time-series and comparative interrupted time-series analyses for interventions introduced at identifiable points in longitudinal data.
-
Apply matching, weighting, fixed-effects, instrumental-variable, and synthetic-control approaches when their underlying identification assumptions are substantively defensible.
-
Conduct robustness, placebo, falsification, sensitivity, and specification analyses to assess how dependent impact estimates are on methodological assumptions.
-
Analyze treatment-effect heterogeneity, staggered interventions, event-study patterns, and modern causal inference challenges in complex observational datasets.
-
Communicate impact evaluation findings transparently by documenting identification strategies, assumptions, statistical methods, uncertainty, limitations, and scope of causal claims.
Comprehensive Course Outline
Module 1: Foundations of Quasi-Experimental Research
-
Principles of causal inference, counterfactual outcomes, treatment effects, intervention exposure, comparison groups, and causal estimands.
-
Differences between randomized experiments, quasi-experiments, observational studies, and descriptive evaluations in terms of identification and assumptions.
-
Understanding treatment assignment mechanisms and how institutional rules, timing, eligibility, geography, thresholds, and other processes can create useful comparison structures.
-
Identifying common threats to causal interpretation including confounding, selection, spillovers, anticipation, attrition, interference, and simultaneous interventions.
Module 2: Research Design and Counterfactual Construction
-
Defining research questions, target populations, interventions, outcomes, treatment timing, estimands, and counterfactual scenarios before statistical analysis.
-
Constructing credible comparison groups using institutional context, historical patterns, eligibility rules, geographic variation, or other sources of quasi-random variation.
-
Assessing baseline comparability, treatment exposure, data availability, measurement quality, and potential sources of systematic differences between groups.
-
Developing causal diagrams and design frameworks to clarify assumptions, pathways, confounders, mediators, and variables that should or should not enter an impact model.
Module 3: Difference-in-Differences and Event Studies
-
Developing difference-in-differences models using repeated observations across treatment and comparison groups before and after an intervention.
-
Assessing the parallel-trends assumption through graphical analysis, pre-treatment comparisons, event-study specifications, and substantive knowledge.
-
Extending difference-in-differences methods to multiple periods, heterogeneous effects, treatment timing differences, and repeated policy implementation.
-
Interpreting event-study coefficients, dynamic treatment effects, pre-treatment patterns, confidence intervals, and limitations associated with staggered adoption.
Module 4: Regression Discontinuity Designs
-
Understanding sharp and fuzzy regression discontinuity designs based on treatment assignment determined by a known eligibility threshold or cutoff.
-
Assessing continuity and local comparability around the threshold while examining bandwidth choices, functional forms, covariates, and sample composition.
-
Detecting possible manipulation, sorting, bunching, measurement problems, and other threats to the validity of discontinuity-based identification.
-
Interpreting regression discontinuity estimates as local effects and distinguishing them from broader population-level intervention effects.
Module 5: Interrupted Time Series and Longitudinal Designs
-
Designing interrupted time-series analyses to evaluate changes in outcome levels and trends following an intervention introduced at a specific time.
-
Extending time-series impact models with comparison series, multiple intervention points, seasonality, autocorrelation, and other temporal structures.
-
Evaluating pre-intervention trends, model specifications, serial dependence, anticipation effects, concurrent events, and changes in measurement processes.
-
Interpreting immediate and gradual intervention effects while distinguishing intervention-related changes from broader temporal patterns.
Module 6: Matching, Weighting and Covariate Adjustment
-
Understanding matching principles, propensity scores, covariate balance, treatment overlap, common support, and limitations of observational adjustment.
-
Applying matching and weighting approaches to construct more comparable treatment and comparison groups based on observed characteristics.
-
Evaluating covariate balance before and after adjustment and identifying situations involving limited overlap, extreme weights, or inadequate adjustment.
-
Combining matching or weighting with regression and longitudinal methods while clearly distinguishing adjustment for observed characteristics from control of unobserved confounding.
Module 7: Instrumental Variables and Synthetic Control Methods
-
Understanding instrumental-variable identification and the relevance, exclusion, and interpretation assumptions required for causal estimation.
-
Distinguishing local treatment effects from population-wide effects when instrumental-variable methods identify effects for particular compliers or affected subgroups.
-
Developing synthetic-control approaches using weighted combinations of comparison units to construct counterfactual trajectories for treated units.
-
Evaluating donor-pool selection, pre-treatment fit, placebo analyses, post-treatment divergence, and sensitivity of synthetic-control estimates.
Module 8: Advanced Impact Analysis and Robustness
-
Evaluating heterogeneous treatment effects across demographic groups, baseline characteristics, treatment intensity, geographic areas, and implementation contexts.
-
Conducting placebo, falsification, sensitivity, permutation, and specification analyses to investigate the credibility and robustness of estimated impacts.
-
Addressing clustering, serial correlation, missing observations, attrition, repeated interventions, treatment contamination, and dependence within longitudinal datasets.
-
Investigating sensitivity to unobserved confounding and understanding the distinction between robustness to specification choices and identification of causal effects.
Module 9: Emerging Quasi-Experimental and Causal Methods
-
Exploring modern difference-in-differences methods for staggered treatment adoption, heterogeneous effects, and varying intervention timing across units.
-
Examining synthetic difference-in-differences, generalized synthetic controls, matrix-completion approaches, and other contemporary counterfactual estimation methods.
-
Introducing causal machine learning approaches for heterogeneous treatment effects, flexible nuisance estimation, and high-dimensional observational research.
-
Using simulation and AI-assisted workflows for design assessment, coding, diagnostics, and documentation while maintaining explicit causal assumptions and researcher oversight.
Module 10: Applied Impact Evaluation and Reporting
-
Developing complete quasi-experimental evaluation workflows from causal question formulation and design selection through estimation, diagnostics, robustness, and interpretation.
-
Selecting appropriate methods according to treatment assignment, timing, comparison structure, outcome characteristics, data availability, and identification assumptions.
-
Preparing publication-quality impact estimates, event-study graphs, treatment-effect tables, balance diagnostics, robustness summaries, and counterfactual visualizations.
-
Reporting quasi-experimental findings transparently by documenting research design, assumptions, data limitations, analytical specifications, uncertainty, treatment-effect scope, and threats to validity.
Training Approach
This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.
Tailor-Made Course
This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: training@upskilldevelopment.com Tel: +254 721 331 808
Training Venue
The training will be held at our Upskill Training Centre. We also offer training for a group (at a discount of 10% to 50%) at requested location all over the world. The Onsite course fee covers the course tuition, training materials, two break refreshments, buffet lunch, airport transfers, Upskill gift package, and guided tour.
Visa application, travel expenses, dinners, accommodation, insurance, and other personal expenses are catered by the participant
Certification
Participants will be issued with Upskill certificate upon completion of this course.
Airport Pickup and Accommodation
Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: training@upskilldevelopment.com, +254 721 331 808
Terms of Payment:
Unless otherwise agreed between the two parties’ payment of the course fee should be done 3 working days before commencement of the training so as to enable us to prepare better.
