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Appendix A: Assessing the Evidence
Pages 445-448

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From page 445...
... For example, researchers who have observed a positive association between student test scores and later adult economic outcomes are careful not to assume a causal relationship between educational performance and employment, due to the possibility of confounders -- variables that are correlated with both test scores and economic outcomes -- causing a spurious association between the two. By contrast, data generated by randomized controlled trials (RCTs)
From page 446...
... Because of these concerns about RCTs, researchers have increasingly used "natural experiments" to estimate causal effects. Such studies harness changes in state and local policies that generate plausibly random or quasirandom variation in exposure to a given service or treatment to estimate its causal effect on outcomes of interest (see Meyer, 1995 and Angrist and Pishke, 2009 for an overview of these methods)
From page 447...
... A researcher using various statistical and econometric techniques can use this variation to generate a causal estimate. Despite the limitations of estimation based on observational data, careful use of observational data has many advantages: First, it is very useful for identifying associations that can be more rigorously studied using other approaches; second, in some cases careful use of natural experiments or other research designs can minimize the bias from confounding; and finally, some questions are by their nature not amenable to randomized trials (such as the question of what the effects are of a new state law that changes the age of majority in the criminal justice system)

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