Causal Inference and Data Science Methods: fixing broken experiments!
My program of research is about developing causal inference methods for large healthcare data, with a particular emphasis on applications of data science tools. See
publications page for more details about each of the following:
Methodological Research:
Use of machine learning / AI approaches in the causal inference context
TMLE and variants
Double machine learning
Autoencoders for dimensionality reduction
Practical issues dealing with large health administrative databases
Longitudinal studies: dealing with data sparseness in Marginal structural models
Incorporating proxy information via high-dimensional propensity score modelling and it’s machine learning versions
Addressing unmeasured confounding via supplimentary information
Time-dependent treatment and time-dependent confounding
Addressing immortal time bias in longitudinal studies
Complex causal diagrams and respective analysis strategies
Mediation analysis: decomposing the effect of direct and indirect effects
Exposure misclassification in observational studies
Pragmatic trials: Incomplete Medication Adherence in Pragmatic trials
Inverse probability of adherence weighting per-protocol effect in sustained treatment strategies
Finite sample properties
Differential adherence
Dealing with data sparseness for post-baseline prognostic factors
Extending the principal stratification framework for multi-category of partial adherence
Comparing the per-protocol effect estimation methods with instrumental methods in point-treatment scenario
Survey data analysis
Incorporating causal inference and data science approaches within the survey data analysis framework
Motivating examples from DHS, NHANES, CCHS.
Real-World Applications
Impact in healthcare
Multiple sclerosis
Tuberculosis
Arthritis
HIV
Opioid use disorder
Spinal cord
Cardiovascular risk
Mental health: depression and mood disorder
Pediatric intensive care
Nephrology and kidney disease
human papillomavirus vaccine
Child Hemoglobin
Antenatal care
Multimorbidity
Residential instability and hospitalizations among homeless and vulnerably housed individuals
Impact in interdisciplinary research
Forestry
Nursing
Building Capacity in Data Scienece and Causal Inference
Proposing guidelines for propensity score analyses in disease specific areas