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propensity-score

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Targeted maximum likelihood estimation (TMLE) enables the integration of machine learning approaches in comparative effectiveness studies. It is a doubly robust method, making use of both the outcome model and propensity score model to generate an unbiased estimate as long as at least one of the models is correctly specified.

  • Updated Jan 5, 2023
  • HTML

Lecture slides, video recordings, and coding exercises from the 2024 Northwestern University Causal Inference Workshop. This repository is not affiliated with Northwestern University or the workshop.

  • Updated Aug 27, 2024
  • Stata

Measuring the effect of diagnostic delays of fungal pathogens on healthcare costs. Linked Marketscan ICD-10 data on symptom and diagnosis dates. Quantile binning probability weighting was used to account for the causal relationship of time. Categorical regressions were conducted for a zero-inflated exposure.

  • Updated Feb 15, 2025
  • R

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