dag-reasoning

name: dag-reasoning description: > Reason about causal structure using directed acyclic graphs for epidemiological studies. Use when identifying confounders, mediators, or colliders; when deciding which variables to adjust for; or when a reviewer questions the adjustment strategy.

DAG-Based Causal Reasoning

Variable classification rules

Before adjusting for any variable, classify its role:

The backdoor criterion

To estimate the causal effect of X on Y:

1. List all paths from X to Y
2. Identify which paths are causal (follow arrow direction) and which are non-causal (backdoor paths)
3. Find a set of variables that blocks all backdoor paths without opening new non-causal paths
4. This set is a valid adjustment set

A path is blocked if it passes through:

• A variable you condition on (if that variable is not a collider on the path)
• A collider you do NOT condition on

Drawing DAGs

Use ASCII art for simple DAGs:

  Confounder
   /      \
  v        v
Exposure --> Outcome

Exposure --> Mediator --> Outcome

Exposure --> Collider <-- Outcome
(DO NOT condition on Collider)

Guidelines:

• Include all variables in the analysis plus important omitted ones
• Every missing arrow is an assumption (no direct effect)
• Time flows left to right where possible
• Mark adjusted variables with brackets: [Variable]

Common pitfalls

Table 2 fallacy

Interpreting coefficients of adjustment variables as if they are causal effects of those variables. Each variable in a model has a different confounding structure; the adjustment set valid for the primary exposure is not valid for other variables.

Overadjustment

Conditioning on a descendant of the outcome, which can induce bias. Example: adjusting for a variable caused by the outcome.

M-bias

Conditioning on a variable that is a common effect of two unobserved causes (one of the exposure, one of the outcome). This opens a path that was previously blocked.

Adjusting for a proxy

Using a proxy for a confounder may not fully block the backdoor path. Residual confounding remains proportional to how poor the proxy is.

Time-varying confounding

When a confounder is itself affected by prior exposure values. Standard regression cannot handle this; requires marginal structural models or g-estimation.

Hierarchical/nested structures

When units are nested (e.g., models within method classes, patients within hospitals):

• Effects can operate at different levels
• Adjusting for group-level variables when the exposure varies at the group level can absorb signal
• Consider whether random effects are appropriate vs fixed effects
• A variable that is constant within clusters cannot confound within-cluster comparisons

Applying DAGs to forecast evaluation

Example for a study of method effects on forecast accuracy:

                Trend
                 |
                 v
Method -----> Accuracy <----- Horizon
  ^              ^               |
  |              |               |
  +-- Location --+               |
  |              |               |
  +---- Time ----+---------------+

• Method -> Accuracy: The effect of interest
• Location, Time, Trend, Horizon -> Accuracy: Prediction difficulty confounders (adjust)
• Location, Time -> Method: If some methods are used in specific locations/periods (adjust)
• Model: Nested within Method; random effect absorbs model-specific skill

Key question: Is "number of forecasts submitted" a confounder, mediator, or collider?

• If more experienced teams (exposure -> submissions) also forecast better (submissions -> outcome): mediator. Do not adjust.
• If better accuracy leads to continued participation: collider on a specific path. Adjusting may bias.