Ehsan Karim

Assistant Professor

Research Interests

Causal inference
Biostatistics
Statistics
Machine Learning
data science
Survey data analysis
multiple sclerosis

Relevant Degree Programs

Affiliations to Research Centres, Institutes & Clusters

Research Options

I am available and interested in collaborations (e.g. clusters, grants).
I am interested in and conduct interdisciplinary research.
I am interested in working with undergraduate students on research projects.
 
 

Biography

Dr. M. Ehsan Karim is an Assistant Professor at the UBC School of Population and Public Health, a Scientist at the Centre for Health Evaluation and Outcome Sciences (CHÉOS), and a Michael Smith Foundation for Health Research (MSFHR) Scholar. He obtained his PhD in Statistics from UBC. He completed his postgraduate training in the Department of Epidemiology, Biostatistics, and Occupational Health at McGill University, and was also a trainee at the Canadian Network for Observational Drug Effect Studies (CNODES). His current research focuses on causal inference and real-world observational data analyses, in both cross-sectional and longitudinal settings; applications of machine learning approaches in the context of electronic healthcare databases; patient-oriented research and survey sampling methodologies in epidemiologic studies.

Research Methodology

Mediation Analysis
Time-dependent confounding
Statistical learning
Longitudinal Data Analysis
Survival Analysis
Observational data analysis
Patient-Oriented Research
High-dimensional propensity score
Marginal structural models
Immortal-time bias
Multiple sclerosis
Super learner
Monte Carlo Simulation
Non-differential Exposure Misclassification
R
Directed Acyclic Graphs
Frailty Model
Bayesian methodologies
Big-Data Analysis
Predictive Modelling
Statistical Computing

Recruitment

Master's students
Doctoral students
Postdoctoral Fellows
Any time / year round
2021
2022

I am currently looking for multiple graduate students/postdocs for the following two projects (with methodologic focuses within epidemiologic contexts): (1) Improving Causal Inference Methods in Statistics for Analyzing High-dimensional / Big Data. (2) Developing and Evaluating Causal Inference Methods for Pragmatic Trials to address nonadherence. Graduate students in statistics, biostatistics, epidemiology, economics or computer science with some methodological expertise in statistics and statistical software are encouraged to contact me directly (particularly those with some of the following skills: making data requests, extracting analytic data from administrative / survey databases, running statistical analyses, coding statistical estimators, conducting simulation studies, excellent scientific writing, ability to work on a multidisciplinary team). Interested candidates should email me (at my UBC email address) their complete CV and a cover letter. Only candidates shortlisted will be contacted.

Graduate students in statistics, biostatistics, epidemiology, economics or computer science with somewhat strong methodological expertise in statistics (as well as statistical computing) are encouraged to contact me directly; particularly those with some of the following skills:

  • making data requests,
  • extracting analytic data from administrative (e.g., health admin) and survey databases (e.g., DHS, NHANES, BRFSS or CCHS),
  • running statistical analyses using standard software (e.g., SAS, R or python),
  • coding statistical estimators (via SAS macro/IML, R, python or stata mata),
  • conducting simulation studies (e.g., in servers, parallel computing, High Performance Computing),
  • excellent scientific writing (e.g., demonstrated via peer-reviewed publications),
  • ability to work on a multidisciplinary team (e.g., work within biostatistics groups).

See details of instructions to apply here.

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Publications

 
 

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