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  • Getting Started
  • User Guide
  • Examples
  • API reference
  • Contributing
  • Release notes
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v0.9
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v0.7
v0.6
v0.5.1
v0.5
v0.4
v0.2
v0.1.1-alpha
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  • Introduction to DoWhy
  • Effect inference
    • 1. Model a causal problem
    • 2. Identify a target estimand under the model
    • 3. Estimate causal effect based on the identified estimand
    • 4. Refute the obtained estimate
    • 5. Comparison to other packages
  • GCM-based inference (Experimental)
    • Introduction
    • Answering Causal Questions
      • Quantifying Arrow Strength
      • Quantifying Intrinsic Causal Influence
      • Simulating the Impact of Interventions
      • Computing Counterfactuals
      • Estimating Average Causal Effects
      • Attributing Distributional Changes
    • Generate samples from a GCM
    • Customizing Causal Mechanism Assignment
    • Estimating Confidence Intervals
    • Independence Tests
  • Citing this package

Answering Causal Questions

In the following sub-sections, we’ll dive deep into all causal questions the GCM-based inference in DoWhy can answer and explain the concepts behind them and how to interpret the results.

  • Quantifying Arrow Strength
  • Quantifying Intrinsic Causal Influence
  • Simulating the Impact of Interventions
  • Computing Counterfactuals
  • Estimating Average Causal Effects
  • Attributing Distributional Changes

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Introduction

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Quantifying Arrow Strength

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