Causal inference studies what would happen under interventions rather than what merely co-moves in observed data. The field connects design, assumptions, and estimation: a randomized trial, a DAG, an IV design, and a weighted estimator are all different ways to argue for the same counterfactual comparison. The core practical question is not whether a model fits well, but whether the identifying assumptions are plausible for the estimand you actually care about. Read the tables as a workflow: define the target effect, map the data-generating process, choose an identification strategy, stress-test the assumptions, and only then optimize estimation.
What This Cheat Sheet Covers
This topic spans 11 focused tables and 109 indexed concepts. Below is a complete table-by-table outline of this topic, spanning foundational concepts through advanced details.
Table 1: Core Frameworks
The potential-outcomes vocabulary — estimands, assumptions, and counterfactuals — is the lingua franca of modern causal inference. Choosing the right estimand (ATE, ATT, CATE, ATO) before touching data prevents the most common framing errors.
| Concept | Example | Description |
|---|---|---|
Y(1), Y(0) observed: Y = TY(1) + (1-T)Y(0) | Counterfactual framework: each unit has an outcome under every possible treatment, only one of which is ever observed. | |
ATE = E[Y(1)-Y(0)] | • Population-average causal effect • the benchmark estimand | |
ATT = E[Y(1)-Y(0) \mid T=1] | • Effect for units that actually received treatment • naturally targeted by matching | |
CATE(x) = E[Y(1)-Y(0) \mid X=x] | • Effect conditional on covariates • the target of heterogeneous-effect methods | |
ATU = E[Y(1)-Y(0) \mid T=0] | • Effect for the control group • relevant when policy would expand coverage to untreated |