Causal Attribution

When examining the impact of a program, a typical analysis will highlight a specific positive change observed in the data. For instance, this could involve increased voter registration or higher election turnout. One of the main challenges in conducting such studies is establishing whether the observed positive change is attributable to the program under investigation, rather than to other contributing factors. Many studies rely on simplistic before-and-after comparisons, which often fail to distinguish between program effects and unrelated temporal changes (e.g., a general increase in public interest in an election). Additionally, some studies compare participants to non-participants, but this approach introduces a significant issue known as selection bias. Those selected to participate in the program, or who choose to participate, may differ from non-participants in various ways, leading to differences that cannot be attributed solely to the program.