Judea Pearl introduces a new framework for defining and measuring direct and indirect effects in both linear and nonlinear models. The paper addresses the challenge of isolating direct effects by holding intermediate variables constant, while indirect effects are more complex due to their dependence on multiple pathways. Pearl proposes a method to assess direct and indirect effects without fixing variables, enabling broader applications in policy analysis. The paper establishes conditions under which these effects can be consistently estimated from experimental and nonexperimental data, extending path-analytic techniques to nonlinear and nonparametric models. The distinction between total, direct, and indirect effects is crucial in causal analysis. Total effects measure the probability of an outcome when an intervention is applied, while direct effects quantify the influence of a variable without mediation. Indirect effects, however, are more challenging to define because they involve multiple pathways and cannot be isolated by fixing variables. Pearl's approach provides a descriptive interpretation of effects, focusing on natural causal relationships rather than controlled experiments, which allows for a broader range of policy-related questions. The paper discusses the policy implications of direct and indirect effects, emphasizing their importance in real-world scenarios such as drug treatment, hiring discrimination, and health care. It highlights the need for a descriptive interpretation that accounts for natural causal paths rather than controlled conditions. The paper also introduces the concept of path-specific effects, which allows for the analysis of effects transmitted through specific pathways in a model. This approach enables the evaluation of complex causal relationships and provides a more accurate understanding of policy impacts. The paper concludes that the new framework for defining direct and indirect effects extends the applicability of these concepts to nonlinear models and provides a more operational interpretation of causal relationships. It emphasizes the importance of considering natural causal paths in policy analysis and highlights the need for further research into the identification of path-specific effects in causal models.Judea Pearl introduces a new framework for defining and measuring direct and indirect effects in both linear and nonlinear models. The paper addresses the challenge of isolating direct effects by holding intermediate variables constant, while indirect effects are more complex due to their dependence on multiple pathways. Pearl proposes a method to assess direct and indirect effects without fixing variables, enabling broader applications in policy analysis. The paper establishes conditions under which these effects can be consistently estimated from experimental and nonexperimental data, extending path-analytic techniques to nonlinear and nonparametric models. The distinction between total, direct, and indirect effects is crucial in causal analysis. Total effects measure the probability of an outcome when an intervention is applied, while direct effects quantify the influence of a variable without mediation. Indirect effects, however, are more challenging to define because they involve multiple pathways and cannot be isolated by fixing variables. Pearl's approach provides a descriptive interpretation of effects, focusing on natural causal relationships rather than controlled experiments, which allows for a broader range of policy-related questions. The paper discusses the policy implications of direct and indirect effects, emphasizing their importance in real-world scenarios such as drug treatment, hiring discrimination, and health care. It highlights the need for a descriptive interpretation that accounts for natural causal paths rather than controlled conditions. The paper also introduces the concept of path-specific effects, which allows for the analysis of effects transmitted through specific pathways in a model. This approach enables the evaluation of complex causal relationships and provides a more accurate understanding of policy impacts. The paper concludes that the new framework for defining direct and indirect effects extends the applicability of these concepts to nonlinear models and provides a more operational interpretation of causal relationships. It emphasizes the importance of considering natural causal paths in policy analysis and highlights the need for further research into the identification of path-specific effects in causal models.