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Update README.md with philosophy #75

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Update README.md 
Signed-off-by: Robert Osazuwa Ness <robertness@gmail.com>
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robertness authored Jan 4, 2023
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Expand Up @@ -43,16 +43,6 @@ Further, DoDiscover seeks to help the user feel confident with their assumptions
Causal representation learning is the task of learning high-level latent variables and the causal structure between them from low-level variables observed in data.
DoDiscover seeks to support causal representation learning algorithms in the context of traditional causal discovery settings.

## Default assumptions of uncertainty

DoDiscover emphasizes representations of causal uncertainty.
These include:
* Representations of equivalent classes of causal graphs
* Uncertainty representation
* Work with graph ensembles
* Bayesian priors and posteriors on graphs
* The possible presence of latent variables

# Documentation

See the [development version documentation](https://py-why.github.io/dodiscover/dev/index.html).
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