Statistics and Causal Inference

作者：Paul W. Holland · 发表于：Journal of the American Statistical Association · 年份：1986 · DOI：10.1080/01621459.1986.10478354 · 被引用次数：5151 · 研究领域：Advanced Causal Inference Techniques、Statistical Methods and Inference、Statistical Methods in Clinical Trials

Problems involving causal inference have dogged at the heels of statistics since its earliest days. Correlation does not imply causation, and yet causal conclusions drawn from a carefully designed experiment are often valid. What can a statistical model say about causation? This question is addressed by using a particular model for causal inference (Holland and Rubin 1983; Rubin 1974) to critique the discussions of other writers on causation and causal inference. These include selected philosophers, medical researchers, statisticians, econometricians, and proponents of causal modeling.