• Home
  • About Us
    • About CRAIL
    • Core Objectives
    • Research Areas
    • Contact Us
  • Our Methods
    • Causal Modeling
    • Intervention Experiments
    • Prediction&Optimization
  • Research Structure
    • Causal Discovery
    • Causal Inference
    • Counterfactual Simulation
    • Intervention Experiments
    • Feedback & Optimization
  • Application Scenarios
    • Healthcare & Medicine
    • Economics & Public Policy
    • Career Development
    • Personal Legal Risk
  • More
    • Home
    • About Us
      • About CRAIL
      • Core Objectives
      • Research Areas
      • Contact Us
    • Our Methods
      • Causal Modeling
      • Intervention Experiments
      • Prediction&Optimization
    • Research Structure
      • Causal Discovery
      • Causal Inference
      • Counterfactual Simulation
      • Intervention Experiments
      • Feedback & Optimization
    • Application Scenarios
      • Healthcare & Medicine
      • Economics & Public Policy
      • Career Development
      • Personal Legal Risk
  • Home
  • About Us
    • About CRAIL
    • Core Objectives
    • Research Areas
    • Contact Us
  • Our Methods
    • Causal Modeling
    • Intervention Experiments
    • Prediction&Optimization
  • Research Structure
    • Causal Discovery
    • Causal Inference
    • Counterfactual Simulation
    • Intervention Experiments
    • Feedback & Optimization
  • Application Scenarios
    • Healthcare & Medicine
    • Economics & Public Policy
    • Career Development
    • Personal Legal Risk

Why Counterfactual Simulation ?

  • Causal Understanding : Helps determine whether an event caused an outcome.
  • Decision Optimization : Assists in making better policy , medical , or business decisions.
  • Ethical & Fair AI  : Detects and corrects biases in machine learniing models.
  • Legal & Responsibility Assessment : Used in courts to assess liability.

Key Concepts in Counterfactual Simulation

  • Counterfactual Model (Rubin Causal Model ) 
  • Structural Causal Models (SCM) & Do-Calculus 
  • Counterfactual Estimation

Methods for Counterfactual Simulation

  • Maching & Reweighting
  • Generative Models & AI 
  • Reinforcement Learning for Counterfactuals

Challenges & Future Directions

  • Unobserved Confounders
  • Computational Complexity
  • Ethical Considerations
  • Causal AI in Decision-Making
  • Personalized Medicine
  • AI-Augmented Policy Simulation

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