STPGA Package v7.0.0 Demo | Package Overview | New Features in v7.0.0 | Modern Criteria Functions | Training Set Size Analysis | Mixed Model Criteria | Unified Interface | Genetic Algorithm Optimization | Single-Objective GA | Multi-Objective GA Example | Summary | Session Information

Introduction | Key Features | Getting Started | Optimization Criteria | Setup for Demonstrations | Classical Optimality Criteria | Prediction Error Variance (PEV) Criteria | Coefficient of Determination (R²) Criteria | Mixed Model Criteria for Genomic Selection | Unified Criterion Interface | Genetic Algorithm Optimization | Enhanced Genetic Algorithm Features (v7.0.0) | Single-Objective Optimization with Advanced Features | Convergence Diagnostics | Multi-Objective Optimization | Advanced Features | Ridge Regularization Effects | Matrix Stability and Conditioning | Practical Guidelines | Training Set Size Recommendations | Criterion Selection Guide | Conclusion | Best Practices | Criterion Selection | Training Set Size | Genetic Algorithm Configuration | Validation and Diagnostics | Genetic Algorithm Examples | Single-Objective Genetic Algorithm | Multi-Objective Genetic Algorithm (NSGA-II) | Comparison: Single vs Multi-Objective | Advanced GA Features Demonstration | Further Reading

1. Lalonde's NSW Benchmark | Data Preparation | Deficiency Estimation | 2. Right Heart Catheterization (RHC) | Data Setup | Quantifying the Information Gap | Policy Regret Bounds | Confounding Frontier

Overview | Part 1: Gene Perturbation Study (Continuous Outcome) | 1.1 Data Description | 1.2 Step 1: Specification | 1.3 Step 2: Deficiency Estimation | 1.4 Step 3: Diagnose with Negative Control | 1.5 Step 4: Policy Decision | 1.6 Effect Estimation | Part 2: Hematopoietic Cell Transplantation (Survival Outcome) | 2.1 Data Description | 2.2 Step 1: Survival Specification | 2.3 Step 2: Deficiency Estimation | 2.4 Step 3: Confounding Frontier | 2.5 Step 4: Policy Regret and RMST Effect | 2.6 Complete Decision Framework | Part 3: Comparative Analysis Across Studies | 3.1 When Is Observational Evidence Sufficient? | 3.2 General Workflow Summary | References

The Core Workflow | Example: Gene Perturbation Analysis | 1. Specification | 2. Deficiency Estimation | 3. Diagnose with Negative Controls | 4. Decision Making | 5. Effect Estimation

Introduction | Theoretical Background | What is a Negative Control Outcome? | The Diagnostic Logic | Negative Control Sensitivity Bound (manuscript thm:nc_bound) | Practical Example | Simulating Data with a Negative Control | Creating the Causal Specification | Running the Negative Control Diagnostic | Interpreting the Results | Scenarios | Scenario 1: Adjustment Succeeds | Scenario 2: Adjustment Fails | Choosing Good Negative Control Outcomes | Ideal Properties | Examples by Domain | Combining with Deficiency Estimation | Advanced: Estimating Kappa | Summary | References

Introduction | Conceptual Translation | The E-value Perspective | The Deficiency Perspective | Conceptual Mapping | Practical Example | Setup | Deficiency Estimation | Confounding Frontier | Policy Regret Bound | Comparison with E-values | Computing an Approximate E-value | Deficiency vs. E-value: Key Differences | When to Use Each | Extended Sensitivity Analysis | Benchmarking Observed Covariates | Combining with Negative Controls | Summary: Unified Sensitivity Analysis | References

Survival Specification | Analysis of HCT Outcomes: Competing Risks | Data Preparation | Specification | Deficiency Estimation | Survival Modeling and RMST | Quantifying Uncertainty: Policy Regret Bounds | Connection to Deficiency

Introduction: The Safety Manifesto | Flash Forward: The Destination | The Core Theory: Markov Kernels in Plain English | Theory Overview: The Car and Destination Analogy | Workflow | Step 1: Data Generation | Step 2: Specification | Step 3: Deficiency Estimation | Interpretation of Deficiency Results: The Distance to Walk | Step 4: Diagnostics (The "Negative Control Trap") | Critical Interpretation: The Deductible | Step 5: Effect Estimation | Step 6: Policy Regret Bounds (Transfer Penalty and Safety Floor) | Decision Framework | Step 7: Sensitivity Analysis & Decision | Go/No-Go Decision | Advanced: Survival Analysis | Conclusion

1. Transportability: Lalonde's Job Training | Transport Deficiency | 2. Policy Learning Bounds: RHC | Policy Evaluation | The Safety Floor

Why Audit Your Data? | Case Study: Right Heart Catheterization (RHC) | Understanding the Research Question | Running the Data Audit | Interpreting the Report | Examining Detected Issues | Clinical Interpretation | Comparing Audit Results Across Subsets | Using Audit Results for Causal Analysis | Full Audit Summary | Best Practices for Data Auditing | Conclusion | References

Introduction | The Safety Floor Concept | Implications for AI/ML Safety | Practical Workflow | Step 1: Define the Causal Problem | Step 2: Estimate Deficiency | Step 3: Visualize Deficiency | Step 4: Compute Policy Regret Bounds | Step 5: Visualize the Safety Floor | Interpreting the Results | The Safety Floor Report | Sensitivity Analysis with Confounding Frontiers | Visualize the Frontier | Policy Learning with grf (Optional) | Best Practices for Safe Deployment | Pre-Deployment Checklist | Monitoring in Production | Mathematical Details | Policy Regret Transfer (Manuscript) | Why This Matters | Summary | References

Introduction | Unobserved Confounding and Negative Controls | Loading the Data | Running the Negative Control Diagnostic | Addressing the Falsification | Sensitivity Analysis: Confounding Frontiers | Visualizing the Frontier | Conclusion