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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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On this page
  • Yoshua Bengio **
  • Judea Pearl
  • Elias Bareinboim **
  • Fei-Fei Li
  • **[Gary Marcus](http://garymarcus.com/index.html) **
  • Miguel Hernan
  • "Causal Inference is Hard"

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  1. Why we need Causality?

Leaders in the Industry

Opinion by the leaders/ People to follow

PreviousWhy we need Causality?NextKey Causal Terms and FAQ

Last updated 4 years ago

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**

(A **co-recipient of the 2018 ACM A.M. Turing Award for his work in deep learning)**

(Awarded with the Turing Award in 2011)

Judea Pearl says AI can’t be truly intelligent until it has a rich understanding of cause and effect, which would enable the introspection that is at the core of cognition.

(Director, Causal Artificial Intelligence Lab CausalAI Laboratory)

(Director of the Stanford Artificial Intelligence Lab)

I believe that today’s machine-learning and AI tools won’t be enough to bring about real AI. “It’s not just going to be data-rich deep learning,” she says. Li believes AI researchers will need to think about things like emotional and social intelligence. -

(Scientist, Author, Entrepreneur)

"In particular, we need to stop building computer systems that merely get better and better at detecting statistical patterns in data sets — often using an approach known as deep learning — and start building computer systems that from the moment of their assembly innately grasp three basic concepts: time, space and causality"

"Causal Inference is Hard"

"I think we need to consider the hard challenges of AI and not be satisfied with short-term, incremental advances. I’m not saying I want to forget deep learning. On the contrary, I want to build on it. But we need to be able to extend it to do things like reasoning, learning causality, and exploring the world in order to learn and acquire information." -

"If you have a good causal model of the world you are dealing with, you can generalize even in unfamiliar situations. That’s crucial. We humans are able to project ourselves into situations that are very different from our day-to-day experience. Machines are not, because they don’t have these causal models." -

**

**[Gary Marcus]() **

MIT Review
MIT Review
Judea Pearl
Elias Bareinboim
Fei-Fei Li
http://garymarcus.com/index.html
Miguel Hernan
Yoshua Bengio