About Me

I’m a self-taught data scientist interested in causal inference and bayesian methods. I mainly use this blog to practice what I learn, but hopefully others find this helpful as well!




Modeling Anything With First Principles: Demand under extreme stockouts

Time Series
Demand Modeling
Causal Inference
Supply Chain
Discrete Choice
Survival Analysis
The Problem: You work at a rental service that is suffering from high periods of churn. You’ve found that stockouts are one of the biggest reasons for churn - despite having…
Jul 9, 2023
32 min

Introduction to Surrogate Indexes

experimentation
Causal Inference
How should you design your experiments if the metric you want to change might take months to observe?
Jul 9, 2023
11 min

Desiging an Experimentation Strategy

experimentation
Experiments have alot more use cases than many give them credit for. At their simplest, they’re a tool for mitigating risk when making product decisions. But at their best…
Jun 12, 2023
16 min

Useful Tools for Weibull Survival Analysis

survival analysis
The Weibull distirbution is an excellent choice for many survival analysis problems - it has an interpretable parameterization that is highly flexible to a large number of…
Oct 31, 2022
13 min

Why do we need A/B tests? The Potential Outcomes Model

experimentation
This blog post introduces the Potential Outcomes Model and introduces why experiments are often necessary to measure what we want. This topic is already covered extensively…
Sep 17, 2022
10 min

Making out of sample predictions with PyMC

A cool thing about hierarchical models is that its easy to predict out of sample - i.e. if you want to make a prediction on a new zipcode, just sample from the state’s…
Apr 17, 2022
6 min

How long should you run an A/B test for?

For some people in industry new to A/B testing, they might wonder “Why cant we just run an A/B test for 2 days and be done with it?”. Even those familiar with it might…
Apr 15, 2022
4 min

Uncertainty Intervals or p-values?

Uncertainty Intervals are better than p-values. Sure, its better to use both, but p-values are just a point estimate and they bring no concept of uncertainty in our estimate…
Apr 11, 2022
7 min

Explainable AI is not Causal Inference

Explainable AI is all the rage these days. Black box ML models come along with some fun tools such as LIME, SHAP, or Partial Depence Plots that try to give visibility into…
Apr 3, 2022
5 min
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