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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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  1. Tools and Libraries

Causal Graphical Models

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Last updated 4 years ago

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The Causal Graphical Model is used to build pretty looking DAGs given the relationship in the form of sets. The model can also be used to get the various relationships between the vertices of the graph and also generate a list of backdoor paths that are valid and can be used in the backdoor adjustment formula while trying to find the relationship between 2 variables.

We have built a collab notebook for you to experiment with building DAGs and to visualize backdoor paths.

Examples:

Causal Graphical Models