• 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

Core objectives

Causal Discovery

Counterfactual Simulation

Intervention Experiments

Identifying hidden causal relationships in complex systems.

Intervention Experiments

Counterfactual Simulation

Intervention Experiments

Designing and testing controlled interventions.

Counterfactual Simulation

Counterfactual Simulation

Counterfactual Simulation

Simulating "what-if"scenarios for decision support.

Optimization & Feedback

Cross-Disciplinary Applications

Counterfactual Simulation

Refining interventions using rel-world feedback.

Cross-Disciplinary Applications

Cross-Disciplinary Applications

Cross-Disciplinary Applications

Applying causal methods across domains like AI, healthcare, economics,wealth and risk.

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