Mistaking the Forest for the Trees

Over the past few decades, the field of political science has become increasingly sophisticated in its use of empirical tests for theoretical claims. One particularly productive strain of this development has been the identification of the limitations of and challenges in using observational data. Making causal inferences with observational data is difficult for numerous reasons. One reason is that one can never be sure that the estimate of interest is un-confounded by omitted variable bias (or, in causal terms, that a given treatment is ignorable or conditionally random). However, when the ideal hypothetical experiment is impractical, illegal, or impossible, researchers can often use quasi-experimental approaches to identify causal effects more plausibly than with simple regression techniques. Another reason is that, even if all of the confounding factors are observed and properly controlled for in the model specification, one can never be sure that the unobserved (or error) component of the data generating process conforms to the assumptions one must make to use the model. If it does not, then this manifests itself in terms of bias in standard errors and incorrect inference on statistical significance of quantities of interest. In this case, one can either turn to standard error "fixes" that are robust to generic forms of deviance from standard assumptions or to non-parametric solutions that do not require such assumptions but may be less powerful than their parametric counterparts. The following essays, I develop the use of some of these techniques for inference with observational data and explore their limitations. Collectively, these essays challenge the conventional application of quasi-experimental techniques and standard error fixes. They also contribute to important substantive debates over legislative organization by producing more cleanly identified effects of the power of Congressional representatives as individuals and as members of parties to bargain over distributive goods.