Deriving the front-door criterion with the do-calculus

A step-by-step mathematical derivation of the front-door criterion in causal inference using the do-calculus, demonstrating how to identify causal effects even with unmeasured confounding.

Decaf vs. regular coffee blinded experiment

A two-week blinded, randomized self-experiment comparing the effects of regular versus decaf coffee on cognitive performance, mood, and alertness, with detailed methodology and quantitative analysis.

All of Statistics - Chapter 3

Selected Exercises #

1. Suppose we play a game where we start with $ c $ dollars. On each play of the game you either double or halve your …

Paper Review - Network Mendelian Randomization

In which I record my thoughts on Network Mendelian Randomization by Burgess et al.

What is this paper about? #

This paper describes a …

Causal Inference Notes

Causal Inference in Statistics #

Questions #

• Why is the causal effect identifiable in an IV DAG when the dependencies are linear (from …

Paper Review - IVs and Mendelian Randomization

Summary #

Detailed Notes #

3 IV Requirements #

1. IV must have a direct influence on the treatment
2. IV must not covary with the …

Matrix Potpourri

Matrix Potpourri #

As part of reviewing Linear Algebra for my Machine Learning class, I’ve noticed there’s a bunch of matrix …

Paper Review - DeepSEA

A review of the DeepSEA paper, which uses convolutional neural networks to predict chromatin features from DNA sequences and evaluate the functional significance of non-coding variants.

Paper Review - Basset

In which I record my thoughts on Basset.

Bio Background #

The genome consists of (broadly) two types of genes, coding genes and noncoding …

Paper Review - DeepBind

A technical review of the DeepBind paper, which uses convolutional neural networks to predict protein-DNA/RNA binding affinities, with analysis of its methods, significance, and potential future extensions.