Part I: Foundations
A/B Testing, Causal Inference, Causal Time Series Analysis, Data Science, Difference-in-Differences, Directed Acyclic Graphs, Econometrics, Impact Evaluation, Instrumental Variables, Heterogeneous Treatment Effects, Potential Outcomes, Power Analysis, Sample Size Calculation, Python and R Programming, Randomized Experiments, Regression Discontinuity, Treatment Effects
You might think causality is just another fancy tool or a twist on statistics. It isn’t. It’s a mindset, a way to structure how we think about change, impact, and what drives what. This first part is less about throwing formulas at you and more about rewiring how you approach data in the real world.
Yes, I’ll introduce the necessary statistical concepts and methods, but the real goal is giving you a new perspective. You’ll start seeing data differently. Here’s a roadmap of what we’ll cover and why it matters for you:
Chapter 1: Data, statistical models, and ‘what is causality’
Key message: Not all data science is about prediction. Machine learning helps forecast what might happen. Causal inference asks: what would’ve happened if we had done something differently?
- Learn the terms: population, sample, outcome, covariate (context variables), parameter, estimate. Nothing fluffy here, think product rollouts, A/B tests, and real business metrics.
- Know the difference between estimand (answer to your causal question), estimator (the method), and estimate (your answer).
- Linear regression isn’t magic - but it’s powerful. Used correctly, it’s a handy starting point for causal claims.
- Correlation can be useful, but causation is what moves businesses. Only one of them tells you what happens when you actually do something.
The payoff: Move beyond guessing. Whether it’s a feature launch or a promo, you’ll learn to reason about what would’ve happened otherwise.
Chapter 2: Biases, causal frameworks, and causal estimands
Key message: Data alone doesn’t cut it. It shows patterns, but not what caused them. That’s where you (and causal thinking) come in.
- Omitted variable bias: You think your feature caused more purchases, but maybe those users were already heavy buyers. Confounding is sneaky.
- Selection bias: Sometimes the way users get into the treatment group is already skewed - and that messes with your conclusions.
- Potential outcomes framework: A clean way to think about “what would’ve happened if…” Since you can’t see both realities for the same person, you need to use smart comparisons.
- ATE (impact for everyone), ATT (impact on those treated), and LATE (impact on the persuadable) - each one answers a different question with a business use case.
The payoff: Catch mistakes before they catch you. You’ll learn to spot flawed conclusions and set up studies that people actually trust.
Chapter 3: Planning and designing credible causal analyses
Key message: Causal analysis doesn’t start with a method. It starts with a sharp question. And a plan to avoid misleading results.
- Learn how to rewrite vague stakeholder asks - like “did our new feature help?” - into clear hypotheses with defined treatments, outcomes, and comparisons.
- Changing your hypothesis after seeing the results is tempting… and dangerous. You’ll learn how to avoid that trap and stay credible.
- Choose the right setup. Randomized tests if you can, smart observational strategies if you can’t (DiD, IVs, RDDs… I’ve got you).
- Don’t run analyses in a vacuum. Ask: what decision does this support? What action does it unlock?
- You’ll see a full example: how to run the analysis, check for fair comparisons (balance), interpret the output, and explain the uplift to your product manager.
The payoff: Credibility by design. You’ll learn to turn vague stakeholder requests into rigorous studies that withstand scrutiny and deliver trusted results.
Why this pays off
Mastering these foundations puts you ahead of most data professionals. You’ll gain the vocabulary to spot questionable conclusions in executive meetings and the confidence to communicate what the data actually can say.
I keep it grounded in realistic examples so you can see how this causal mindset drives business growth. These aren’t just basics; they are the prerequisites for everything that follows. Let’s get started.