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  • Emory University
  • Atlanta, GA
  • 12:07 (UTC -12:00)

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annaguo-bios/README.md

Welcome to My GitHub Profile!

Hi, I’m Anna. I’m a PhD candidate in Biostatistics at Emory.

  • 👩 My personal website
  • 🔭 I’m broadly interested in causal inference and machine learning, with a focus on:
    • Causal perspectives on missing data
    • Flexible estimation of causal effects using targeted learning and double de-biased machine learning
    • Identification of causal effects in the presence of unmeasured confounding

Projects

  • Sufficient Identification Conditions and Semiparametric Estimation under Missing Not at Random Mechanisms. 📚code. 📄paper.
  • Targeted Machine Learning for Average Causal Effect Estimation Using the Front-Door Functional. 📚code. 📦package. 📄paper.
  • Average Causal Effect Estimation in DAGs with Hidden Variables: Extensions of Back-Door and Front-Door Criteria. 📚code. 📦package. 📄paper.

Pinned Loading

  1. criss-cross-model-code criss-cross-model-code Public

    Implementation of experiments for paper titled "Sufficient identification conditions and semiparametric estimation under missing not at random mechanisms"

    R

  2. fdcausal fdcausal Public

    An R Package for Average Causal Effect Estimation via the Front-Door Functional

    R 2

  3. fd-methods fd-methods Public

    Implementing experiments in paper titled "Targeted Machine Learning for Average Causal Effect Estimation Using the Front-Door Functional"

    R

  4. ADMGs-Estimation-paper ADMGs-Estimation-paper Public

    Average Causal Effect Estimation in DAGs with Hidden Variables: Extensions of Back-Door and Front-Door Criteria

    TeX 1

  5. flexCausal flexCausal Public

    flexCausal is a flexible and robust tool for estimating causal effects via double de-biased ML using observational data, where the relationships between observed and unmeasured variables can be spe…

    R 9 1