1: Background & Motivation

Causal Inference is a field that touches several domains and is of interest to a wide range of practitioners. These include Statisticians, Data Scientists, Machine Learning Scientists, and other Computational Researchers.

To date I have written several pieces on methods/topics in the Causal Inference space. These include:

1: Introduction & Motivation

In parametric Bayesian Inference, our objective is to recover the posterior distribution of the parameter (or parameters) of interest. …

1: Introduction & Motivation

Let’s say we’re working on an inferential statistics analysis. Assume we’re working from a frequentist perspective. …

1: Introduction & Motivation

In this piece, I look to cover the mathematical underpinnings of Maximum Likelihood Estimation (MLE); a commonly used procedure for constructing sampling estimators for parameters of interest of a distribution. Though very commonly used, MLE is a procedure not always well understood or well motivated mathematically from a teaching perspective.

1: Introduction

For linear smoothers and linear-predictor based sampling estimators, Mercer Kernels are a highly convenient tool for fitting linear decision boundaries in high dimensional feature spaces. In fact, such feature spaces can even be infinitely dimensional (as we will show). From the perspective of Machine Learning, Mercer Kernels can be viewed…

1: Introduction

Boosting is a family of ensemble Machine Learning techniques for both discrete and continuous random variable targets. Boosting models take the form of Non-Parametric Additive models and are most typically specified with additive components being “weak learners”. …

1: Introduction

Generalized Linear Models (GLMs) play a critical role in fields including Statistics, Data Science, Machine Learning, and other computational sciences.

Part I of this Series provided a thorough mathematical overview with proofs of common GLMs, both in Canonical and Non-Canonical forms. Part II provided historical and mathematical context of common…

1: Background and Motivation

Generalized Linear Models (GLMs) play a critical role in fields including Statistics, Data Science, Machine Learning, and other computational sciences.

In Part I of this Series, we provided a thorough mathematical overview (with proofs) of common GLMs both in Canonical and Non-Canonical forms. …

1: Background and Motivation

Generalized Linear Models (GLMs) play a critical role in fields including Statistics, Data Science, Machine Learning, and other computational sciences. This class of models are a generalization of ordinary linear regression for certain response variable types with error distribution models other than a normal distribution.

To refresh our memories on…

Causal Inference in Data Science: A/B Testing & Randomized Trials with Covariate Adjustment

Efficiency & Statistical Power gains from Conditional Covariate Adjustment in A/B Testing & Randomized Trials 