Table of Contents

• Guide to Causal Inference

• About Us

• Preface

• What is Causality?

• Why bother with Causality?

• Origin of Causality

• Statistical Inference Vs Causal Inference

• Decision-Making

• Why we need Causality?

  • Leaders in the Industry

• Key Causal Terms and FAQ

• Assumptions

  • Causal Assumptions

• Bias

  • Selection Bias

  • Correlation is not Causation

    • Simpsons Paradox

• Causal Graphs

  • Structural Causal Model

  • do-calculus

  • Graph Theory

  • Build your DAG

  • Testable Implications

  • Limitations of Causal Graphs

  • Colliders

  • Mediators

  • Confounders

  • Back Door Paths

  • Front Door Paths

• Counterfactuals

  • Potential Outcomes Framework

• Modeling for Causal Inference

  • Experimental Data

    • Randomization

      • A/B Testing

        • Experiment

  • Non-Experimental / Observational Data

    • Instrumental Variables

    • Weighting

      • Inverse Propensity Weighting

    • Propensity Score

    • Sensitivity Analysis

    • Regression Discontinuity

    • Matching

    • Stratification

      • Methods

      • Implications

• Tools and Libraries

  • DoWhy

    • Do-Sampler

    • EconML

    • Workflow

  • Causal Graphical Models

  • CausalInference

  • Dagitty

  • Other Libraries

• Limitations of Causal Inference

  • Fundamental Problem of Causal Inference

• Real-World Implementations

• What's Next

• References