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
Tools and Libraries
Limitations of Causal Inference
- Fundamental Problem of Causal Inference
Real-World Implementations
What's Next
References

Powered by GitBook

On this page

Tools and Libraries

CausalInference

PreviousCausal Graphical Models NextDagitty

Last updated 5 years ago

Was this helpful?

CtrlK

Was this helpful?

The CausalInference package of Python allows us to generate the values of Adjustment Treatment Effect, Propensity Score, and also enable stratification and improving co-variate balance.

Have a look at the co-lab notebook below to see how the CausalInference package can be used to determine the best Backdoor Adjustment path for a causal model.

Examples: Causal Graphical Models

Resources:

CausalInference Documentation
Degenerate State Blog on Backdoor Adjustment