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Introduction to Causal Inference - Part 3
Continuing in the series on causal inference, this post discusses analyzing the results of a pair matched trial design with Wilcoxon's signed rank test and how to compute approximate p-values via normal approximation.
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Building Decision Trees with the ID3 Algorithm
For a machine learning course, I had to write code to implement the ID3 algorithm to train decision trees from scratch. Writing recursive functions can be challenging and even frustrating, particularly when you are a math/stats master's student just beginning his foray into the world of devops and computer science. Each piece of the unoptimized recursion I wrote is written out in gory detail here for your reading pleasure.
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Introduction to Causal Inference - Part 2
Continuing in the causal inference series, this post discusses pair matched trial design via propensity scores and the "naive" model of observational studies.
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Introduction to Causal Inference - Part 1
This is my master's thesis broken into smaller, more digestible pieces. Causal inference is a fascinating (and relatively emergent) branch of statistics that seeks to establish causal relationships between variables. It turns out that establishing causality is intensely more demanding than establishing associations via traditional statistical inference methods. This post covers the groundwork to get started with causal inference, including essential background about randomized experiments and observational studies.
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The Gradient Descent Algorithm
The gradient descent algorithm turns up nearly everywhere in machine learning. This algorithm is intensely popular because it is excellent at solving certain types of optimization problems. It must be used thoughtfully, however, since it is not guaranteed to converge to global extrema. It's absolutely essential for machine learning engineers to understand the mathematics of this ubiquitous algorithm.