Causal Inference Cheat Sheet

Updated 2026-03-19

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 92 indexed concepts. Below is a complete table-by-table outline of this topic, spanning foundational concepts through advanced details.

Table 1: Core FrameworksTable 2: Graphs And IdentificationTable 3: Experimental DesignTable 4: Adjustment And Propensity ScoresTable 5: Matching And WeightingTable 6: Instruments And ComplianceTable 7: Quasi-Experimental DesignsTable 8: Panel And Time-Series ExtensionsTable 9: Longitudinal And MediationTable 10: Sensitivity And Bias ChecksTable 11: ML, Discovery, And Software

Table 1: Core Frameworks

Concept	Example	Description
Potential Outcomes	$Y(1), Y(0)$ `observed: Y = TY(1) + (1-T)Y(0)`	Counterfactual framework for defining causal effects.
Average Treatment Effect	$ATE = E[Y(1)-Y(0)]$	Population-average effect.
Average Treatment Effect on the Treated	$ATT = E[Y(1)-Y(0) \mid T=1]$	Effect for units that actually received treatment.
Conditional Average Treatment Effect	$CATE(x) = E[Y(1)-Y(0) \mid X=x]$	Effect conditional on covariates.

Table 1: Core Frameworks

Concept	Example	Description
Potential Outcomes	$Y(1), Y(0)$ `observed: Y = TY(1) + (1-T)Y(0)`	Counterfactual framework for defining causal effects.
Average Treatment Effect	$ATE = E[Y(1)-Y(0)]$	Population-average effect.
Average Treatment Effect on the Treated	$ATT = E[Y(1)-Y(0) \mid T=1]$	Effect for units that actually received treatment.
Conditional Average Treatment Effect	$CATE(x) = E[Y(1)-Y(0) \mid X=x]$	Effect conditional on covariates.