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Blackwell, Matthew

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Blackwell

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Blackwell, Matthew

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2013 - 20152

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Author's Publications: search.filters.isAuthorOfPublication.a4d7f482-8090-4f08-b4fb-eb34e0dba74c

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    A Selection Bias Approach to Sensitivity Analysis for Causal Effects
    (Oxford University Press (OUP), 2013) Blackwell, Matthew
    The estimation of causal effects has a revered place in all fields of empirical political science, but a large volume of methodological and applied work ignores a fundamental fact: most people are skeptical of estimated causal effects. In particular, researchers are often worried about the assumption of no omitted variables or no unmeasured confounders. This paper combines two approaches to sensitivity analysis to provide researchers with a tool to investigate how specific violations of no omitted variables alter their estimates. This approach can help researchers determine which narratives imply weaker results and which actually strengthen their claims. This gives researchers and critics a reasoned and quantitative approach to assessing the plausibility of causal effects. To demonstrate the approach, I present applications to three causal inference estimation strategies: regression, matching, and weighting.
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    A Unified Approach to Measurement Error and Missing Data: Details and Extensions
    (SAGE Publications, 2015) Blackwell, Matthew; Honaker, James; King, Gary
    We extend a unified and easy-to-use approach to measurement error and missing data. Black-well, Honaker and King (2014) gives an intuitive overview of the new technique, along with practical suggestions and empirical applications. Here, we other more precise technical details; more sophisticated measurement error model specifications and estimation procedures; and analyses to assess the approach's robustness to correlated measurement errors and to errors in categorical variables. These results support using the technique to reduce bias and increase efficiency in a wide variety of empirical research.