Causal Inference Framework for Scientific Experiments

Develop a comprehensive causal inference framework for scientific experimental design that can handle complex, observational research scenarios. Create a modular system integrating directed acyclic graphs, potential outcomes modeling, and machine learning techniques to estimate causal effects in multifaceted research contexts. Implement advanced methods like propensity score matching, instrumental variable analysis, and sensitivity analysis to quantify causal relationships. Generate interactive visualizations and statistical reports demonstrating causal pathways and uncertainty estimates.