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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

DoWhy

A causal Inference Library from Microsoft

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Last updated 4 years ago

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DoWhy is a Python Library for Causal Inference from Microsoft, developed by , . Its name is inspired by Judea Pearl’s do-calculus for causal inference.

About

DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical models and potential outcomes frameworks. DoWhy stresses on Interpretability of the output. At any point in the analysis, you can inspect the untested assumptions.

DoWhy uses the Bayesian graphical Model framework where one can specify all the assumptions about the data generating process.

DoWhy is a very effective and helpful library for implementing Causal Inference. The library implements causality by first making the underlying assumptions explicit, for example, by explicitly representing identified estimands. Secondly by making sensitivity analysis and other robustness checks on these explicit assumptions, for example, by introducing a confounder or by replacing the intervention with placebo. The goal here is to enable people to focus their efforts on identifying assumptions for causal inference, rather than on details of estimation.

Resource:

DoWhy examples:

Github
https://github.com/Microsoft/dowhy
https://microsoft.github.io/dowhy/
Amit Sharma
Emre Kiciman