![]() ![]() We then assess these models’ performance in a series of neutral simulations, in which they produce substantial (55% to $$90%) reduction in bias relative to competing models. ![]() We describe a category of models, flexible joint likelihood models, that account for both features of the data while avoiding reliance on rigid functional-form assumptions. Heckman-style multiple-equation models offer a solution to this problem however, they rely on functional-form assumptions that can produce substantial bias in estimates of average treatment effects. Matching methods, widely considered to be the state of the art in causal inference in political science, are generally ill-suited to inference in the presence of unobserved confounders. Measuring the causal impact of state behavior on outcomes is one of the biggest methodological challenges in the field of political science, for two reasons: behavior is generally endogenous, and the threat of unobserved variables that confound the relationship between behavior and outcomes is pervasive. ![]()
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