CAUSAL INFERENCE, PATH ANALYSIS AND RECURSIVE STRUCTURAL EQUATIONS MODELS

Abstract

Rubin's model for causal inference in experiments and observational studies is enlarged to analyze the problem of “causes causing causes” and is compared to path analysis and recursive structural equations models. A special quasi‐experimental design, the encouragement design, is used to give concreteness to the discussion by focusing on the simplest problem that involves both direct and indirect causation. Rubin's model is shown to extend easily to this situation and to specify conditions under which the parameters of path analysis and recursive structural equations models have causal interpretations.