README.md

Medium Blog Posts

Hi all! 👋

In this repository, I collect drafts and published Jupyter Notebooks for blog posts on my website and Medium. I write to learn and help others.

If you find mistakes or you have suggestions for new posts, let me know! Hug 🤗

Posts

• Understanding Instrumental Variables

  How to estimate causal effects when you cannot randomize treatment

• Beyond Churn Prediction and Churn Uplift

  How to best target policies in the presence of churn

• Evaluating Uplift Models

  How to compare and select the best uplift model

• From Causal Trees to Forests

  How to use random forests to do policy targeting

• Understanding Causal Trees

  How to use regression trees to estimate heterogeneous treatment effects

• Bayesian AB Testing

  Using and choosing priors in randomized experiments

• Experiments on Returns on Investment

  An introduction to the delta method for inference on ratio metrics

• Mean vs Median Causal Effect

  An introduction to quantile regression in A/B tests

• A/B Tests, Privacy, and Online Regression

  How to run experiments without storing individual-level data

• Outliers, Leverage, Residuals, and Influential Observations

  What makes an observation “unusual”?

• The Bayesian Bootstrap

  A short guide to a simple and powerful extension of the bootstrap

• Understanding Synthetic Control Methods

  A detailed guide to one of the most popular causal inference techniques in the industry

• Understanding AIPW, the Doubly-Robust Estimator

  A guide to the estimation of conditional average treatment effects (CATE) under model misspecification

• Understanding Meta Learners

  How to use machine learning to estimate heterogeneous treatment effects

• Matching, Weighting, or Regression?

  Understanding and comparing different methods for conditional causal inference analysis

• Understanding CUPED

  An in-depth guide to the state-of-the-art variance reduction technique for A/B tests

• How to Compare Two or More Distributions

  A complete guide to comparing distributions, from visualization to statistical tests

• Understanding Contamination Bias

  Problems and solutions of linear regression with multiple mutually exclusive treatments

• Double Debiased Machine Learning (part 2)

  How to remove regularization bias using post-double selection

• Double Debiased Machine Learning (part 1)

  Causal inference, machine learning, and regularization bias

• Understanding Omitted Variable Bias

  A step-by-step guide to the most pervasive type of bias

• Understanding The Frisch-Waugh-Lovell Theorem

  A step-by-step guide to one of the most powerful theorems in causal inference

• Goodbye Scatterplot, Welcome Binned Scatterplot

  How to visualize and do inference on conditional means

• Experiments, Peeking, and Optimal Stopping

  How to run valid experiments with smaller sample sizes with the Sequential Probability Ratio Test

• DAGs and Control Variables

  How to select control variables for causal inference using Directed Acyclic Graphs