When considering model selection criteria for nested statistical models, AIC and BIC usually comes to our mind. These two metrics are derived base on the maximized log-likelihood value with a tradeoff of number of predictors.

k is number of predictors (image from )



R library


1. Level Set

1a. Final Goal: Goal to create a counterfactual dataset

Ideally, we want a counterfactual data for each observation:

for i = 1, …, n:
- Outcome if treated: Yi(A=1)
- Outcome if control: Yi(A=0)

However, in reality, we only observe 1 outcome for each observation

1b. What does TMLE comes from?

P(Y, A, W) = P(Y|A, W) * P(A|W) * P(W)

If we just do regular regression Y ~ A + W, then in order to have…

Miao Wang

A Newbie Data Scientist in NYC

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