Impact evaluation of multiple overlapping programs under a conditional independence assumption

Under the assumption on conditional independence between potential outcomes and program assignment, program impacts measured by the Average Treatment Effect (ATE) and the Average Treatment Effect on Treated (ATT) can be identified and estimated using cross-section regression or propensity score matching (PSM). Traditional impact literature often deals with the impact evaluation of a single program. In reality, one can participate in several programs simultaneously and the programs may be correlated. This paper discusses cross-section regression and PSM methods in this general context. It is shown that under the PSM method, impact of a program of interest can be measured as a weighted average of program impacts on groups with different program statuses. Estimation of impacts of multiple overlapping programs is illustrated using Monte Carlo simulation and an empirical example of impact measurement of international and internal remittances in Vietnam.