Methods of discovering causal effects: Difference between revisions

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| The [[Do calculus|''do'' calculus]] || This allows us to infer causal effects just by observation (i.e. without conducting an experiment). I think this one breaks down into further strategies. Three of them are listed in p. 30 of <ref>Stephen L. Morgan; Christopher Winship. ''Counterfactual and Causal Inference: Methods and Principles for Social Research''. 2nd ed. Cambridge University Press. 2015.</ref> ||
| The [[Do calculus|''do'' calculus]] || This allows us to infer causal effects just by observation (i.e. without conducting an experiment). I think this one breaks down into further strategies. Three of them are listed in p. 30 of <ref>Stephen L. Morgan; Christopher Winship. ''Counterfactual and Causal Inference: Methods and Principles for Social Research''. 2nd ed. Cambridge University Press. 2015.</ref> ||
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==See also==
* [[Creation of causal graphs]]


==References==
==References==


<references/>
<references/>

Revision as of 19:54, 31 October 2018

Methods of discovering causal effects: basically, after observing or interacting with the environment, how can we discover what causes what? When are we justified to claim causal effects (not just correlation)?

Method/strategy Notes Example
Try to deliberately control as many other variables as possible [1] ?
Randomized controlled trial [1] Michael Nielsen also talks about this in his tutorial There are lots of examples of this. It would be good to explain a concrete one. For now, I'll just link to this section on Wikipedia.
The do calculus This allows us to infer causal effects just by observation (i.e. without conducting an experiment). I think this one breaks down into further strategies. Three of them are listed in p. 30 of [2]

See also

References

  1. 1.0 1.1 Cosma Rohilla Shalizi. "Identifying Causal Effects from Observations". April 7, 2016.
  2. Stephen L. Morgan; Christopher Winship. Counterfactual and Causal Inference: Methods and Principles for Social Research. 2nd ed. Cambridge University Press. 2015.