Ehsan Karim

Assistant Professor

Research Interests

Causal inference
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.


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
Directed Acyclic Graphs
Frailty Model
Bayesian methodologies
Big-Data Analysis
Predictive Modelling
Statistical Computing


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

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).

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Graduate Student Supervision

Doctoral Student Supervision (Jan 2008 - April 2022)
Tuberculosis survivor health (2021)

BACKGROUND: Improvements in and expansion of tuberculosis (TB) diagnosis and treatment have yielded a growing population of TB survivors, with an estimated 155 million alive in 2020. While TB is preventable and curable, there is accumulating evidence of elevated chronic disease risk among survivors. Research objectives: (1) estimate the relative risk of non-TB mortality among TB survivors compared with controls, (2) systematically review the literature on cardiovascular disease (CVD) in TB and latent TB infection, (3) estimate the relative risk of airway disease among respiratory TB survivors compared with controls, and (4) estimate the relative risk of depression among TB survivors compared with controls, mediated by hospital length of stay (LOS).METHODS: A cohort of immigrants to British Columbia, Canada, 1985-2015, with linked health administrative and TB registry data was used for retrospective cohort studies of TB survivor health. Cox proportional hazards (PH) and time-varying models were used in statistical analyses. Causal mediation analysis of depression, mediated by hospital LOS, estimated depression risk. A prospectively registered systematic review and random-effects meta-analysis of TB and CVD was performed. RESULTS: In a time-varying Cox regression of non-TB mortality, an adjusted hazard ratio (aHR) of 1.69 (95% CI:1.50-1.91) was observed between TB exposed and non-TB exposed time. In the systematic review and meta-analysis, we found increased risk of major adverse cardiovascular events (MACE) among TB patients compared with non-TB controls (pooled RR = 1.51; 95% CI: 1.16-1.97). A higher risk of airway disease among respiratory TB survivors compared with non-TB controls was observed in our Cox PH regression (aHR=2.08; 95% CI: 1.91-2.28). In the causal mediation analysis of depression, TB survivors had aHR=1.24 (95% CI: 1.14-1.34) for depression by TB, decomposed into a natural direct effect of aHR=1.11 (95% CI: 1.02-1.21) and indirect effect of aHR=1.11 (95% CI: 1.10-1.12), indicating 50% (95% CI: 35-82%) mediation through hospital LOS.CONCLUSION: TB survivors face higher mortality from non-TB causes, and higher risk of airway disease, CVD, and depression, compared with non-TB controls. Chronic disease screening and models of care development are needed to support TB survivors’ health-related quality of life, during and after TB treatment.

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Master's Student Supervision (2010 - 2021)
Incorporating partial adherence into the principal stratification analysis framework (2019)

Participants in pragmatic clinical trials often partially adhere to treatment. In the presence of partial adherence, simple statistical analyses of binary adherence (receiving either full or no treatment) introduce biases. We developed a framework which expands the principal strati cation approach to allow partial adherers to have their own principal stratum and treatment level. We derived consistent estimates for bounds on population values of interest. A Monte Carlo posterior sampling method was derived that is computationally faster than Markov Chain Monte Carlo sampling, with con firmed equivalent results. Simulations indicate that the two methods agree with each other and are superior in most cases to the biased estimators created through standard principal strati cation. The results suggest that these new methods may lead to increased accuracy of inference in settings where study participants only partially adhere to assigned treatment.

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