Postdoc: Causal Inference for Longitudinal Health Data

University of Texas MD Anderson Cancer Center

Houston (TX)

On-site

USD 60,000 - 90,000

Full time

14 days+
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Job summary

The University of Texas MD Anderson Cancer Center in Houston seeks a postdoctoral fellow to advance causal inference methods for long-term pharmacotherapy in breast cancer care. You will contribute to theory, estimation, and real-world applications across large healthcare databases and international trial data, under PI guidance.

Responsibilities include developing estimands, efficient estimators, and open-source R tools, with opportunities to publish and present at major conferences; offsite

Qualifications

  • Ph.D. in Biostatistics, Statistics, or Epidemiology with a strong focus on causal inference methods.
  • Strong programming skills in R and SAS; experience using large administrative claims datasets.

Responsibilities

  • Develop and formalize causal estimands using counterfactual theory, causal DAGs, and single world intervention graphs.
  • Derive efficient influence functions and TMLE algorithms for patient-choice protocols and related models.
  • Implement doubly robust, semi-parametric estimators with machine learning in high-dimensional longitudinal data.
  • Develop and apply sensitivity analysis methods.
  • Analyze large-scale observational claims data and contribute to open-source software in R.
  • Prepare manuscripts and present at national and international conferences.

Skills

Causal inference
R programming
SAS
Healthcare claims data

Education

PhD in Biostatistics, Statistics, or Epidemiology

Tools

R
SAS

Job description

The University of Texas MD Anderson Cancer Center in Houston seeks a postdoctoral fellow to advance causal inference methods for long-term pharmacotherapy in breast cancer care. You will contribute to theory, estimation, and real-world applications across large healthcare databases and international trial data, under PI guidance.

Responsibilities include developing estimands, efficient estimators, and open-source R tools, with opportunities to publish and present at major conferences; offsite

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