Causal inference is a mindsetCausal inference from observational data is a mindset, not a set of tools.Jan 4, 2023Jan 4, 2023
Published inTDS ArchiveCausal Inference with Continuous TreatmentsGeneralizing inverse probability weights for non-categorical treatmentsNov 2, 2022A response icon1Nov 2, 2022A response icon1
Why we care for covariate balancing in comparative studiesBalancing variables in statistical comparative analysis is a proxy, not a goal.Nov 20, 2021Nov 20, 2021
A 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
Published inTDS ArchiveUsing 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, 2020A response icon1Dec 28, 2020A response icon1
The 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
Published inTDS ArchiveSolving 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, 2020A response icon4Feb 22, 2020A response icon4
Applying Deep Learning to Genetic PredictionWhat classical methods for obtaining polygenic (risk) scores lack, and how deep learning might help mitigated these shortcomings.May 5, 2018A response icon1May 5, 2018A response icon1
