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Commending you on so skillfully navigating the waters of DAGs and PO. That the two are compatible comes from the fact that both are derived from structural causal models (SCM). DAGs are used to encode what we know and PO what we wish to know. However, I find it hard to
Commending you on so skillfully navigating the waters of DAGs and PO. That the two are compatible comes from the fact that both are derived from structural causal models (SCM). DAGs are used to encode what we know and PO what we wish to know. However, I find it hard to
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understand why you say that "PO are most useful for estimation". Assuming that we have obtained an estimand using DAG-based identification, isn't the estimand itself sufficient for estimation? Do we really need to dress it in PO cloths before proceeding with the estimation
understand why you say that "PO are most useful for estimation". Assuming that we have obtained an estimand using DAG-based identification, isn't the estimand itself sufficient for estimation? Do we really need to dress it in PO cloths before proceeding with the estimation
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phase? This dressing habit, I believe, is a remnant of bygone age when, lacking DAGs, people attempted to identify queries of interest in the PO language. But why go through that tormented experience today, when we do have DAGs.??? #Bookofwhy
phase? This dressing habit, I believe, is a remnant of bygone age when, lacking DAGs, people attempted to identify queries of interest in the PO language. But why go through that tormented experience today, when we do have DAGs.??? #Bookofwhy
