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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

Sensitivity Analysis

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Last updated 4 years ago

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Explanation

Sensitivity analysis is a modeling technique, performed to analyze the effect of independent variables on a particular dependent variable under a given set of assumptions. Sensitivity analysis is used very often in the business world and in the field of economics. It is commonly used by financial analysts and economists and is also known as a what-if analysis.

In the world of Causal Inference, the approach of sensitivity analysis directly models confounding variables or selection biases.

It is especially useful in the study and analysis of a “Black Box Process” where the output is an opaque function of several inputs. An opaque function or process is one which, for some reason, can’t be studied and analyzed. For example, climate models in geography are usually very complex. As a result, the exact relationship between the inputs and outputs are not well understood.

An example of a situation where a 'What-If' analysis would be needed is:

"What would happen to my final grade if were to study 4 hours extra every day?"

Resource :

Identification, Inference and Sensitivity Analysis for Causal Mediation Effects
causalsens: Sensitivity Analysis for Causal Effects
Sensitivity Analysis