Quasi-experimental methods are research designs that that aim to identify the impact of a particular intervention, program, or event (a "treatment") by comparing treated units (households, groups, villages, schools, firms, etc.) to control units.
Quasi-Experimental Methods
An event study is a statistical method to assess the impact of an event on an outcome of interest. It can be used as a descriptive tool to describe the dynamic of the outcome of interest before and after the event or in combination with regression discontinuity techniques around the time of the event to evaluate its impact.
Selection bias occurs when participants in a program (treatment group) are systematically different from non-participants (control group). Selection bias affects the validity of program evaluations whenever selection of treatment and control groups is done non-randomly.
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The synthetic control method is a statistical method to evaluate treatment effects in comparative case studies. It creates a synthetic version of treated units by weighting variables and observations in the control group.
The difference-in-differences method is a quasi-experimental approach that compares the changes in outcomes over time between a population enrolled in a program (the treatment group) and a population that is not (the comparison group). It is a useful tool for data analysis.
Matching is a quasi-experimental method in which the researcher uses statistical techniques to construct an artificial control group by matching each treated unit with a non-treated unit of similar characteristics.
Instrumental variables (IV) estimation is a quasi-experimental approach that overcomes endogeneity through the use of a valid instrument.
Propensity score matching (PSM) is a quasi-experimental method in which the researcher uses statistical techniques to construct an artificial control group by matching each treated unit with a non-treated unit of similar characteristics. Using these matches, the researcher can estimate the impact of an intervention.
Regression Discontinuity Design (RDD) is a quasi-experimental impact evaluation method used to evaluate programs that have a cutoff point determining who is eligible to participate. RDD allows researchers to compare the people immediately above and below the cutoff point to identify the impact of the program on a given outcome. This page will cover when to use RDD, sharp vs.