Inference in tough places : essays on modeling and matching with applications to civil conflict

Abstract

This dissertation focuses on the challenges of making inferences from observational data in the social sciences, with particular application to situations of violent conflict. The first essay utilizes quasi-experimental conditions to examine the effects of violence against civilians in Darfur, Sudan on attitudes towards peace and reconciliation. The second and third essays both address a common but overlooked challenge to making inferences from observational data: even when unobserved confounding can be ruled out, correctly "conditioning on" or "adjusting for" covariates remains a challenge. In all but the simplest cases, existing methods ensure unbiased estimation only when the investigator can correctly specify the functional relationship between covariates and the outcome. The second essay (with Jens Hainmueller) introduces Kernel Regularized Least Sqaures (KRLS), a flexible modelling approach that provides investigators with a powerful tool to estimate marginal effects, without linearity or additivity assumptions, and at low risk of misspecification bias. The third essay introduces Kernel Balancing (KBAL), a weighting method that mitigates the risk of misspecification bias by establishing high-order balance between treated and control samples without balance testing or a specification search.