Primary Submission Category: Applicants in Social Sciences
A Bayesian State-Space Approach with Dynamic Covariates for Disentangling Anticipatory and Intervention Effects
Authors: Damiano Baldaccini, Alessandra Mattei, Fiammetta Menchetti,
Presenting Author: Damiano Baldaccini*
Evaluating public policies is particularly challenging when their nationwide scope precludes the existence of a suitable control group and when advance announcements generate anticipatory effects that must be disentangled from the effects of the policy itself. We address these challenges within the potential outcomes framework. We formally define the causal estimands of interest, explicitly distinguishing between the effects of the policy announcement and those of the policy implementation. We then introduce a set of identifying assumptions under which these causal effects can be estimated using a Bayesian state-space model that exploits the time-series structure of the data. The proposed model is estimated over the full time horizon and incorporates a dynamic treatment variable that captures the exogenous shocks induced by both the policy announcement and the subsequent intervention. Through a series of simulation studies, we assess the performance of our approach and compare it with standard counterfactual forecasting methods for causal inference in the absence of a control group. Finally, we apply the proposed methodology to evaluate an Italian transportation policy that incentives green vehicles and penalizes polluting ones.