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  • Getting Started
  • User Guide
  • Examples
  • dowhy package
  • Contributing
  • Release notes
  • Citing this package
Releases
v0.9.1
v0.9
v0.8
v0.7.1
v0.7
v0.6
v0.5.1
v0.5
v0.4
v0.2
v0.1.1-alpha
Branches
main
  • Foreword
  • Introduction to DoWhy
  • Modeling Causal Relations
    • Specifying a causal graph using domain knowledge
    • Learning causal structure from data
    • Refuting a Causal Graph
      • Performing independence tests
      • Graph refutations
  • Modeling Graphical Causal Models (GCMs)
    • Types of graphical causal models
    • Generate samples from a GCM
    • Customizing Causal Mechanism Assignment
    • Estimating Confidence Intervals
  • Performing Causal Tasks
    • Estimating Causal Effects
      • Identifying causal effect
        • Backdoor criterion
        • Frontdoor criterion
        • Natural experiments and instrumental variables
        • ID algorithm for discovering new identification strategies
      • Estimating average causal effect using backdoor
        • Regression-based methods
        • Distance-based matching
        • Propensity-based methods
        • Do-sampler
      • Estimating average causal effect with natural experiments
      • Estimating conditional average causal effect
      • Estimating average causal effect using GCM
      • Estimating direct and indirect effects
        • Estimating natural direct and indirect effects
        • Direct Effect: Quantifying Arrow Strength
    • Explaining Observed Effects and Root-Cause Analysis
      • Anomaly Attribution
      • Attributing Distributional Changes
      • Quantifying Intrinsic Causal Influence
      • Unit Change Attribution
      • Feature Relevance
    • Asking and Answering What-If Questions
      • Simulating the Impact of Interventions
      • Computing Counterfactuals
    • Predicting outcome for out-of-distribution inputs
  • Refuting causal estimates
    • Refuting Effect Estimates
      • Placebo Treatment Refuter
      • Dummy Outcome Refuter
      • Random Common Cause Refuter
      • Data Subsample Refuter
      • Sensitivity Analysis
        • Simulation-based sensitivity analysis
        • Partial-R2 based sensitivity analysis for linear estimators
        • Reisz estimator-based sensitivity analysis for non-linear estimators
  • Citing this package

Estimating direct and indirect effects

In addition to average total causal effect, DoWhy can be used to estimate direct and indirect effects. More generally, these tasks form a part of mediation analysis.

  • Estimating natural direct and indirect effects
  • Direct Effect: Quantifying Arrow Strength

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Estimating average causal effect using GCM

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Estimating natural direct and indirect effects

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