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An overview of methods to evaluate the effectiveness of models in finding heterogeneous treatment effects in randomized control trials (“uplift models”), introducing two novel evaluation curves: the adjusted Qini curve and the efficiency curve. Read the full paper (PDF) Abstract Uplift models seek to estimate individual treatment effects, helping practitioners answer questions like “who should we target with our treatment” rather than simply “what is the individual treatment effect”. This paper provides a comprehensive overview of evaluation methods for such models, with particular focus on:...

Often, data collection cannot be completely random – e.g. in clinical trials, where it would be unethical to randomly treat people with medicine, or in online surveys, where response cannot be guaranteed. In such cases, data can be biased, so any inferences drawn or machine learning models built from this data will not generalize well to the overall population. This is where unbiased estimation can come in, in which small adjustments are effectively made to the dataset to make it more representative of a random sample....

My PhD thesis on emergent geometric complexity in natural systems, using river networks as a rich example of how simple constituent interactions produce novel structures and statistical properties across multiple scales. Download PDF (65 MB) | MIT DSpace Abstract This thesis explores emergence—how novel structures arise from collective interactions between constituent entities—through the lens of river network geometry. River networks exemplify emergent complexity: simple physical processes (erosion, diffusion, pressure gradients) interact to produce geometric patterns and power-law statistics that appear fundamentally different from their microscopic origins....