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Validating Causal Estimates in Experimental and Observational Designs


Social scientists and policy makers continue to put increased emphasis on identifying causal effects in their research, employing the myriad of novel approaches that have been developed in recent years. With this rise in the use of causal analysis tools, the importance of understanding the assumptions underlying such methods has increased. Methods and estimates are occasionally stretched beyond the limits of the underlying assumptions. This has increased the need for a thorough understanding of what assumptions are necessary to identify causal estimates outside of a purely experimental framework. Additionally, it has lead to a need for validation tests that allow researchers to provide sound evidence that estimates are unbiased.

This dissertation focuses on one area, methods for increasing the external validity of experimental estimates, of interest to researchers and policy makers alike. This problem is addressed from multiple angles, both theoretical, practical, and from a design perspective. First, I outline the assumptions necessary to identify population treatment effects from experimental data. I then discuss a new estimating strategy for testing the key identifying assumption central to most causal methods, the notion of exchangeability. This new method leverages tests of equivalence to redefine the approach to validation tests such as balance and placebo tests. Finally, a new approach to improving the underlying data from which we explore questions and estimate quantities of interest is provided. This new method, response rate sampling, allows researchers to collect more representative survey data. Combined, these tools will allow researchers to move more fluidly between experimental and observational estimates.

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