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Understanding Causal Inference
  • A Guide to Causal Inference
  • Table of Contents
  • 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
    • Colliders
    • Confounders
    • Mediators
    • Back Door Paths
    • Front Door Paths
    • Structural Causal Model
    • do-calculus
    • Graph Theory
    • Build your DAG
    • Testable Implications
    • Limitations of Causal Graphs
  • Counterfactuals
    • Potential Outcomes Framework
  • Modeling for Causal Inference
    • Experimental Data
      • Randomization
        • Problems with 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
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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

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Last updated 4 years ago

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