Dynamic Causal Inference and Impact Measurement

Design a comprehensive causal inference framework that can rigorously measure the true impact of interventions across complex systems. Develop methodologies combining potential outcomes framework, graphical causal models, and machine learning techniques to estimate treatment effects while accounting for selection bias, confounding variables, and heterogeneous treatment effects. Include strategies for handling observational data, managing model uncertainty, and generating interpretable causal insights.