Adjusted methods (Backdoor criterion)
- A set of approaches used to control for confounding and isolate the causal effect of an exposure on an outcome.
- Based on the backdoor criterion, these methods require a valid adjustment set to block non-causal paths.
- The key premise is that the causal graph is known or assumed.
- These methods rely on adjusting for confounding variables using conditioning sets.
Overlap: Widely overlaps with Causal ML (which builds upon these), Causal Discovery (which may infer the graph), and Quasi-Experiments (when combined with temporal or spatial structure)
Key feature: Control for confounding through observed covariates
Usage: Widely used when experimental data is unavailable but good covariate data exists