❓
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
Powered by GitBook
On this page

Was this helpful?

  1. Modeling for Causal Inference

Experimental Data

Experimental Study

PreviousModeling for Causal InferenceNextRandomization

Last updated 4 years ago

Was this helpful?

Experimental Studies are ones where we do a real intervention on the population and study the effect of that intervention. The population is divided into "Treatment" and "Control" group where one receives the treatment and other does not. Then the effect on the population is compared and studied to make causal Conclusions. It is considered a very powerful method to get causation.

Fact: Experiments and empirical observations have been the most important part of the , which allows engineers and scientists to innovate by making some hypotheses, gathering data on them, and making a decision based on these hypotheses. Experimentation here plays a very important role in collecting data to test hypotheses and help in the decision-making process as part of scientific method.

Scientific Methods