Causal inference is the most important aim in social science. Over the last 30 years, we have witnessed a plenty of conceptual and methodological improvements to deal with causal inference in statistics, social and biomedical sciences. This paper aims to provide an overview of causal modeling, which includes the counterfactual conception of causality, potential outcomes framework, and statistical models commonly used in social and biomedical sciences. Additionally, some recent issues regarding mediation analysis and structural equation models are discussed.
주제어: Causal Inference, Mean Structural Model, Matching, Mediation