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y0-causal-inference / y0

❓y0 (pronounced "why not?") is for causal inference in Python
https://y0.readthedocs.io
BSD 3-Clause "New" or "Revised" License
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Notes Dump #222

Open cthoyt opened 5 months ago

cthoyt commented 5 months ago

Here's the remaining notes from my work document that weren't categorized:

probability of Y given X -> regress Y on x probability of Z

  1. how to incporporate the adjustment set how do you figure out what parameters of linear structural causal model? do this for every edge:
  2. what's source/target
  3. regress target on source + adjustment set
  4. coefficient associated with source is the one you use

  5. any conditionals as union on to the adjustment set

    front door adjustment that's more complicated napkin is identifiable, but you can still get an estimand estimand to linear regression -> you actually have to pile several of them together the generic approach is the plugin method. For discrete data, you can just go for it. Continuous data is harder chiro made a bayesian version of doubly robust estimation - that's the general way of most > efficiently making use of data