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#causality #statistics

In contrast, the non-strict causal edges assumption would allow for some parents to not be causes of their children. It would just assume that children are not causes of their parents. This allows us to draw graphs with extra edges to make fewer assumptions, just like we would in Bayesian networks, where more edges means fewer independence assumptions.

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In contrast, the non-strict causal edges assumption would allow for some parents to not be causes of their children. It would just assume that children are not causes of their parents. This allows us to draw graphs with extra edges to make fewer assumptions, just like we would in Bayesian networks, where more edges means fewer independence assumptions. Causal graphs are sometimes drawn with this kind of non-minimal meaning, but the vast majority of the time, when someone draws a causal graph, they mean that parents are causes of their

#### Original toplevel document (pdf)

owner: crocodile - (no access) - Introduction_to_Causal_Inference-Nov19_2020-Neal.pdf, p31

In contrast, the non-strict causal edges assumption would allow for some parents to not be causes of their children. It would just assume that children are not causes of their parents. This allows us to draw graphs with extra edges to make fewer assumptions, just like we would in Bayesian networks, where more edges means fewer independence assumptions. Causal graphs are sometimes drawn with this kind of non-minimal meaning, but the vast majority of the time, when someone draws a causal graph, they mean that parents are causes of their

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