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Causal Inference 101
Foundations
What causal inference is, why it matters, and the vocabulary you need.
What is causal inference?
6 min readA clear introduction to causal inference — how we estimate cause-and-effect from data and why it differs from ordinary statistics.
Correlation vs causation
5 min readWhy correlation is not enough for decisions, with concrete examples of misleading associations and how causal thinking fixes them.
Why causal inference matters for decisions
5 min readHow causal estimates improve allocation, policy, medicine, and product strategy — and when they save you from acting on noise.
Treatment, outcome, and confounders
5 min readThe core variables in any causal study — defined clearly with examples from medicine, marketing, and policy.
Study design
Experiments, observational data, and when each approach is appropriate.
Assumptions & bias
Confounding, identification, and what must hold for causal claims.
Confounding explained
6 min readWhat confounding is, how it biases treatment effects, and the main strategies to address it.
Introduction to causal graphs (DAGs)
5 min readHow directed acyclic graphs clarify confounding, mediators, and colliders — without heavy math.
Covariate balance and why it matters
4 min readHow to read balance tables and standardized mean differences after matching or weighting.
The parallel trends assumption (DiD)
4 min readWhat parallel trends means, how to assess it visually, and what to do when it is doubtful.
Methods
Practical estimators from matching to causal forests and discovery.
Propensity score matching explained
7 min readHow propensity scores balance treated and control groups — and when matching is a good choice.
Inverse probability weighting (IPW)
5 min readHow IPW reweights observations to create a pseudo-population where treatment is independent of confounders.
Difference-in-differences (DiD)
7 min readEstimate causal effects when a policy or product change hits some groups before others — using before/after and treated/control comparisons.
Instrumental variables — a practical introduction
6 min readHow IV methods isolate causal effects when treatment is confounded but a valid instrument exists.
Heterogeneous treatment effects & causal forests
6 min readWhy average effects hide who benefits most — and how modern ML estimators find subgroups with different responses.
Uplift modeling for marketing
5 min readTarget customers who respond because of your campaign — not those who would convert anyway.
Causal discovery basics
5 min readWhen you do not know the causal graph upfront — algorithms that suggest structure from data, and how to use them carefully.
DoWhy and refutation tests
5 min readUnify assumptions, estimation, and sensitivity checks — including placebo and random confounder tests.
Doubly robust estimation
4 min readWhy combining propensity and outcome models can protect you when one model is slightly wrong.
Application
Choosing methods, validating results, and using estimates in decisions.
Robustness and sensitivity analysis
5 min readHow strong would hidden confounding need to be to overturn your conclusion? Practical sensitivity thinking.
How to choose a causal method
7 min readA decision guide: match your data structure, treatment type, and assumptions to the right estimator.
Causal inference in medicine & health
6 min readTreatment effects, EHR data, confounding by indication, and communicating results to clinical audiences.
Causal inference in marketing
5 min readCampaign lift, incrementality, holdout tests, and uplift targeting for better spend allocation.
Causal inference in public policy
5 min readProgram evaluation, DiD around legislation, and evidence standards for policymakers.
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