Ehud KaravaniCausal inference is a mindsetCausal inference from observational data is a mindset, not a set of tools.Jan 4, 2023Jan 4, 2023

Ehud KaravaniinTowards Data ScienceCausal Inference with Continuous TreatmentsGeneralizing inverse probability weights for non-categorical treatmentsNov 2, 2022Nov 2, 2022

Ehud KaravaniWhy we care for covariate balancing in comparative studiesBalancing variables in statistical comparative analysis is a proxy, not a goal.Nov 20, 2021Nov 20, 2021

Ehud KaravaniA visual way to think of macro and micro averages in classification metricsExplaining what macro-average and micro-average metrics are.Sep 4, 2021Sep 4, 2021

Ehud KaravaniinTowards Data ScienceUsing machine learning metrics to evaluate causal inference modelsReinterpreting known machine learning evaluations from a causal inference perspective, focusing on ROC curves for propensity models.Dec 28, 20201Dec 28, 20201

Ehud KaravaniThe Case Against Agile in ResearchEver popular iterative development approaches can sneak in unconscious-bias that can be harmful to the scientific process.Jul 6, 2020Jul 6, 2020

Ehud KaravaniinTowards Data ScienceSolving Simpson’s Paradox with Inverse Probability WeightingA visual intuition on how the most popular method in causal-inference works, and how it solves the most popular paradox in statistics.Feb 22, 20204Feb 22, 20204

Ehud KaravaniApplying Deep Learning to Genetic PredictionWhat classical methods for obtaining polygenic (risk) scores lack, and how deep learning might help mitigated these shortcomings.May 5, 20181May 5, 20181