analyzing-causal-dag

To choose the right control variables for causal inference, you must apply the backdoor criterion to a predefined causal graph, explicitly excluding post-treatment variables and colliders to isolate valid adjustment sets.

Regression coefficient sign flips after adding controls often indicate collider bias or improper adjustment for a mediator. Analyzing your causal graph reveals whether included variables open backdoor paths or block causal transmission.

In a causal DAG, a confounder opens a backdoor path between treatment and outcome, a mediator transmits the treatment effect, and a collider is caused by both, meaning adjusting for it induces bias.

Determining valid adjustment sets requires mapping temporal order and directed edges in a causal graph, then screening covariates against the backdoor criterion to exclude descendants of the treatment that induce collider bias.

Yes, analyzing a causal DAG supports instrumental variable edge cases by mapping directed edges and temporal order, helping you distinguish instruments from confounders and avoid invalid adjustments in observational effect estimation.

You should run sensitivity analysis for observational effect estimates after finalizing your adjustment set, using E-values or tipping-point checks to report the robustness of your causal assumptions against unmeasured confounding.