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 🤗
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Understanding Instrumental Variables
How to estimate causal effects when you cannot randomize treatment
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Beyond Churn Prediction and Churn Uplift
How to best target policies in the presence of churn
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How to compare and select the best uplift model
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How to use random forests to do policy targeting
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How to use regression trees to estimate heterogeneous treatment effects
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Using and choosing priors in randomized experiments
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Experiments on Returns on Investment
An introduction to the delta method for inference on ratio metrics
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An introduction to quantile regression in A/B tests
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A/B Tests, Privacy, and Online Regression
How to run experiments without storing individual-level data
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Outliers, Leverage, Residuals, and Influential Observations
What makes an observation “unusual”?
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A short guide to a simple and powerful extension of the bootstrap
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Understanding Synthetic Control Methods
A detailed guide to one of the most popular causal inference techniques in the industry
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Understanding AIPW, the Doubly-Robust Estimator
A guide to the estimation of conditional average treatment effects (CATE) under model misspecification
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How to use machine learning to estimate heterogeneous treatment effects
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Matching, Weighting, or Regression?
Understanding and comparing different methods for conditional causal inference analysis
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An in-depth guide to the state-of-the-art variance reduction technique for A/B tests
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How to Compare Two or More Distributions
A complete guide to comparing distributions, from visualization to statistical tests
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Understanding Contamination Bias
Problems and solutions of linear regression with multiple mutually exclusive treatments
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Double Debiased Machine Learning (part 2)
How to remove regularization bias using post-double selection
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Double Debiased Machine Learning (part 1)
Causal inference, machine learning, and regularization bias
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Understanding Omitted Variable Bias
A step-by-step guide to the most pervasive type of bias
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Understanding The Frisch-Waugh-Lovell Theorem
A step-by-step guide to one of the most powerful theorems in causal inference
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Goodbye Scatterplot, Welcome Binned Scatterplot
How to visualize and do inference on conditional means
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Experiments, Peeking, and Optimal Stopping
How to run valid experiments with smaller sample sizes with the Sequential Probability Ratio Test
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How to select control variables for causal inference using Directed Acyclic Graphs
