We develop semiparametric tests for conditional independence in time series models of causal effects. Our approach is motivated by empirical studies of monetary policy effects and is semiparametric in the sense that we model the process determining the distribution of treatment—the policy propensity score—but leave the model for outcomes unspecified. A conceptual innovation is that we adapt the cross-sectional potential outcomes framework to a time series setting. We also develop root-T consistent distribution-free inference methods for full conditional independence testing, appropriate for dependent data and allowing for first-step estimation of the (multinomial) propensity score.

We study the consequences of an exogenous upsurge in coca prices and cultivation in Colombia, where most coca leaf is now harvested. This shift generated only modest economic gains in rural areas, primarily in the form of increased self-employment earnings and increased labor supply by teenage boys. The rural areas that saw accelerated coca production subsequently became considerably more violent, while urban areas were affected little. These findings are consistent with the view that the Colombian civil conflict is fueled by the financial opportunities that coca provides and that rent-seeking by combatants limits the economic gains from coca.