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on 03-Jan-2025 (Fri)

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Flashcard 7545578196236

Tags
#DAG #causal #edx
Question
Inverse probability [...] is in fact just one of the group of so called G-methods
Answer
weighting

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Inverse probability matching is in fact just one of the group of so called G-methods

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Flashcard 7628373232908

Tags
#DAG #causal #edx
Question
So all these methods for confounding adjustment -- stratification, matching, inverse probability weighting, G-formula, G-estimation -- have two things in common. First, they require data on the [...] that block the backdoor path. If those data are available, then the choice of one of these methods over the others is often a matter of personal taste. Unless the treatment is time-varying -- then we have to go to G-methods
Answer
confounders

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So all these methods for confounding adjustment -- stratification, matching, inverse probability weighting, G-formula, G-estimation -- have two things in common. First, they require data on the <span>confounders that block the backdoor path. If those data are available, then the choice of one of these methods over the others is often a matter of personal taste. Unless the treatment is time-vary

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Flashcard 7628979309836

Tags
#deep-learning #keras #lstm #python #sequence
Question
Batch : A pass through a subset of samples in the training dataset after which the [...] are updated. One epoch is comprised of one or more batches
Answer
network weights

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Batch : A pass through a subset of samples in the training dataset after which the network weights are updated. One epoch is comprised of one or more batches

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Flashcard 7673834245388

Tags
#abm #agent-based #machine-learning #model #priority #synergistic-integration
Question
Semisupervised learning falls in between supervised or unsupervised learning algorithms. It is an approach that combines a small amount of labeled data with a large amount of unlabeled data during training when the cost of [...] work may render large, fully labeled training sets infeasible, whereas the acquisition of unlabeled data is relatively inexpensive.
Answer
labeling

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in between supervised or unsupervised learning algorithms. It is an approach that combines a small amount of labeled data with a large amount of unlabeled data during training when the cost of <span>labeling work may render large, fully labeled training sets infeasible, whereas the acquisition of unlabeled data is relatively inexpensive. <span>

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Annotation 7673843420428

#causal #inference

The G-methods family, as developed by James Robins and colleagues, specifically includes:

  1. Inverse probability weighting (IPW)
  2. G-computation
  3. G-estimation of structural nested models

Inverse probability matching (propensity score matching) is not technically a G-method. While it shares the fundamental goal of addressing confounding and selection bias, it employs a different mathematical framework and estimation approach
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