Causal Effects via the Do-operator | Overview & Example

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This is the 3rd video in a series on causal effects. Here I discuss a new way to formulate the average treatment effect (ATE) using the do-operator. This alternative formulation unlocks new paths toward estimating causal effects from observational data.

Resources:

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Introduction - 0:00
Observational vs Interventional Data - 0:35
2 Formulations of ATE - 2:23
do-operator - 5:26
Identifiability - 7:05
Truncated Factorization Formula - 10:34
Coping with Unmeasured Confounders - 10:52
Interventional Distribution via Parents - 12:34
Key Points - 13:08

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Комментарии

Very helpful! what is the package for truncated factorization formula, any sample code like your other videos! Thanks for sharing all of this!

afroozansaripour

That Bayes probability at 10:26 (sum over z), shouldn't P(Z) on the right handed side be conditional probability as well?

baluga-sf

Why no pause between sentences? Annoying to listen to. But great content, thanks!

karannchew

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