패널 데이터에서 인과 추론을 위한 이중 차분법의 활용

This study discusses the theoretical background and empirical analysis of the Difference-in-Differences (DID) method and the Doubly Robust DID (DRDID) estimator in causal inference using panel data. In causal inference, treatment effects cannot be directly measured, necessitating appropriate assumptions, with the Stable Unit Treatment Value Assumption (SUTVA) being fundamental. SUTVA assumes no interference between units and that the same treatment always produces the same outcome. However, in panel data, temporal dependencies and treatment interference may violate this assumption. The DID method, based on the Parallel Trends Assumption, considers both temporal differences and group-level differences simultaneously, making it a powerful tool in causal inference. However, the DID estimate obtained from a mixed effects model may significantly deviate from the true value, necessitating an alternative approach such as the DRDID estimator. DRDID has the advantage of providing a consistent estimator as long as either the outcome regression model or the propensity score model is correctly specified. Verifying the consistency and efficiency of the DRDID method through simulation, this study conducts an empirical analysis using the Korean Aging Study Panel Data to estimate the DRDID estimator for treatment effects within the DID framework.