Causal Inference

A Causal Inference is a logical inference based on causal relationships.

• Context:
  • It can (often) be associated with a Question of Cause.
  • It can range from being a Prediction to being a Postdiction.
  • It can be produced by a Causal Inference System (solving a causal inference task).
  • …
• Example(s):
  • …
• Counter-Example(s):
  • a Correlational Inference.
  • a Counterfactual Inference.
  • a Statistical Inference.
• See: Fixed Effects Regression, Causal Relation, Association (Statistics), Instrumental Variables Estimation.

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References

2020

• (Wikipedia, 2020) ⇒ https://en.wikipedia.org/wiki/causal_inference Retrieved:2020-12-15.
  • Causal inference is the process of drawing a conclusion about a causal connection based on the conditions of the occurrence of an effect. The main difference between causal inference and inference of association is that the former analyzes the response of the effect variable when the cause is changed. The science of why things occur is called etiology. Causal inference is an example of causal reasoning.

2018

• (Pearl & Mackenzie, 2018) ⇒ Judea Pearl, and Dana Mackenzie. (2018). “The Book of Why: The New Science of Cause and Effect.” Hachette UK. ISBN:9780465097609
  • QUOTE: … it is important to understand the achievements that causal inference has tallied thus far. We will explore the way that it has transformed the thinking of scientists in almost every data-informed discipline and how it is about to change our lives.
  • The new science addresses seemingly straightforward questions like these:
    • How effective is a given treatment in preventing a disease?
    • Did the new tax law cause our sales to go up, or was it our advertising campaign?
    • What is the health-care cost attributable to obesity?
    • Can hiring records prove an employer is guilty of a policy of sex discrimination?
    • I’m about to quit my job. Should I?
  • These questions have in common a concern with cause-and-effect relationships, recognizable through words such as “preventing,” “cause,” “attributable to,” “policy,” and “should I.” Such words are common in everyday language, and our society constantly demands answers to such questions. Yet, until very recently, science gave us no means even to articulate, let alone answer, them.

2015

• (Sharma et al., 2015) ⇒ Amit Sharma, Jake M. Hofman, and Duncan J. Watts. (2015). “Estimating the Causal Impact of Recommendation Systems from Observational Data.” In: Proceedings of the Sixteenth ACM Conference on Economics and Computation.

2001

• (Shadish et al., 2001) ⇒ William R. Shadish, Thomas D. Cook, and Donald T. Campbell. (2002). “Experimental and Quasi-Experimental Designs for Generalized Causal Inference, 2nd edition.” Wadsworth Publishing. ISBN:0395615569
  • http://depts.washington.edu/methods/readings/Shadish.pdf

2000

• (Pearl, 2000) ⇒ Judea Pearl. (2000). “Causality: Models, reasoning, and inference." Cambridge University Press. ISBN:0521773628.

1986

• (Holland, 1986) ⇒ Paul W. Holland. (1986). “Statistics and Causal Inference.” In: Journal of the American Statistical Association, 81(396).
  • http://www.jstor.org/stable/2289064

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