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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. Modeling for Causal Inference
  2. Non-Experimental / Observational Data
  3. Weighting

Inverse Propensity Weighting

IPW or inverse probability Weighting drawn from the concept of weighting itself. In this process, we initially consider the probability of every person being assigned to a treatment or control group. If some part of the population that has a higher probability of being in the treatment group, is assigned to the control group, are very valuable. Here we would want to weigh-up this population in the control group, and weigh-down the majority of the population which actually went into the treatment group.

Example:

w(x)=1p(x)w(x)=11−p(x)w(x)= \frac{1}{p(x)}\\ w(x)= \frac{1}{1-p(x)}w(x)=p(x)1​w(x)=1−p(x)1​
PreviousWeightingNextPropensity Score

Last updated 4 years ago

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