Difference between revisions of "Quasi-Experimental Methods"

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==Read First==
==Read First==
*Common examples of quasi-experimental methods include [[Difference-in-Differences | difference-in-differences]], [[Regression Discontinuity | regression discontinuity design]], [[Instrumental Variables | instrumental variables]] and [[Matching | matching]]. For more details on each type, please visit their respective pages.
*Common examples of quasi-experimental methods include  
**[[Difference-in-Differences | Difference-in-differences]]  
**[[Regression Discontinuity | Regression discontinuity design]]
**[[Instrumental Variables | Instrumental variables]]  
**[[Propensity Score Matching|Propensity score matching]]  
**[[Matching | Matching]]
*In general, quasi-experimental methods require larger [[Sampling|sample sizes]] and more assumptions than [[Experimental Methods|experimental methods]] in order to provide valid and unbiased estimates of program impacts.
*In general, quasi-experimental methods require larger [[Sampling|sample sizes]] and more assumptions than [[Experimental Methods|experimental methods]] in order to provide valid and unbiased estimates of program impacts.



Latest revision as of 19:07, 9 August 2023

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. While quasi-experimental methods use a control group, they differ from experimental methods in that they do not use randomization to select the control group. Quasi-experimental methods are useful for estimating the impact of a program or event for which it is not ethically or logistically feasible to randomize. This page outlines common types of quasi-experimental methods.

Read First

Overview

Like experimental methods, quasi-experimental methods aim to estimate program effects free of confoundedness, reverse causality or simultaneous causality. While quasi-experimental methods use a counterfactual, they differ from experimental methods in that they do not randomize treatment assignment. Instead, quasi-experimental methods exploit existing circumstances in which treatment assignment has a sufficient element of randomness, as in regression discontinuity design or event studies; or simulate an experimental counterfactual by constructing a control group as similar as possible to the treatment group, as in propensity score matching.

Assumptions and Limitations

In general, quasi-experimental methods require larger samples than experimental methods. Further, for quasi-experimental methods to provide valid and unbiased estimates of program impacts, researchers must make more assumptions about the control group than in experimental methods. For example, difference-in-differences relies on the equal trends assumption, while matching assumes identical unobserved characteristics between the treatment and control groups.

Additional Resources