confounding-assessment

Identifying confounding variables requires enumerating potential factors for a specified exposure-outcome pair, assessing their measurability and adjustability, and constructing a causal directed acyclic graph (DAG) to visualize causal relationships.

Collider bias occurs when adjusting for a variable influenced by both the exposure and outcome. You can check for colliders in proposed adjustment strategies by evaluating the causal DAG structure to avoid distorting the true effect.

Quantifying unmeasured confounding in epidemiology involves computing an informal E-value. This metric estimates the minimum strength of association an unmeasured confounder must have with both exposure and outcome to explain away the observed effect.

Yes, you can generate a visual causal DAG for bias assessment. Constructing the directed acyclic graph utilizes networkx for structural graph logic and matplotlib for rendering the visual representation of causal relationships.

Yes, this confounding assessment approach works for evaluating drug effectiveness with observational data. It systematically identifies and accounts for pre-existing conditions or demographics that might distort the true effect of the exposure.

The informal E-value provides a heuristic estimate rather than a formal statistical bound for unmeasured confounding. It indicates the potential impact of unmeasured factors but requires careful interpretation alongside the causal DAG and measured confounders.