Causal Inference from the Ground up

Welcome to Causal Inference from the Ground up! In this series, I’m documenting my own journey of “writing to learn” as I explore the foundations of causal inference. My goal is to move beyond just defining terms and instead build a solid, practical framework for how to approach causal questions. Each post is a building block, designed to connect core theory with practical intuition and real-world examples. Whether you’re a data scientist, analyst, or just curious about the science of cause and effect, I hope you’ll find this exploration helpful.

📚 Posts in This Series

Below you’ll find all posts in the Causal Inference from the Ground Up series, organized by date.

EP3: The Ladder of Causation: Why Predicting Isn’t the Same as Changing Outcomes

causal inference

identification

causal hierarchy

You spend three months building a model. You have petabytes of user data. You ship a feature because your model says X and Y move together. You are confident.

EP2: The Bridge to Truth: Why Identification Comes Before Estimation

causal inference

identification

estimand

In the first episode of causal inference from the ground up series, we talked about the 3 core trios: estimand (quantity of interest), estimate (a concrete number), and an es…

EP1: The Core Trio: Estimand, Estimator, and Estimate

causal inference

estimation

estimand

estimate

estimator

Want to dive deeper into the causal trio? This podcast episode provides more in-depth information and intuition about the estimand, estimator, and estimate. So do tune in!