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. 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. 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. 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. 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. Finally, we demonstrate their utility in a reanalysis of Simmons’ (2000) classic study of the impact of Article VIII commitment on compliance with the IMF’s currency-restriction regime.

One of the U.S. Department of Agriculture Rural Development's most popular programs is the funding for public water and sewer infrastructure projects in rural communities. This article reviews the water and sewer infrastructure projects funded in the state of Oklahoma between 1990 and 2000 and evaluates their impact on different measures of economic growth over both the short (one to 10 years) and long (10 to 20 years) term. Evaluation techniques include multivariate regression and average treatment effects. Results suggest that although most economic growth measures (population, income levels, and poverty levels) are not impacted by the program, housing values do show a statistically significant increase in communities receiving water or sewer infrastructure funding over the long term.