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causal-inference #

Side-by-side animated DAGs contrast observational conditioning P(Y|X=x) with intervention P(Y|do(X=x)). A confounder U creates a backdoor path that biases observation; the do-operator visually cuts incoming arrows into X, leaving only the causal effect X→Y. The visualization also hints at identification via backdoor adjustment when U is observed.

canvasclick to interact

⏮◀◀▶▶STEP0.25x1xZOOM

t=0s

practical uses #

• 01.Estimating treatment effects from observational data (e.g., medicine, policy)
• 02.Diagnosing confounding and choosing adjustment sets with DAGs
• 03.Deciding when observational correlations can identify causal effects (do-calculus/backdoor)

technical notes #

Pure Canvas2D, retro grid-snapped primitives. Two panels crossfade every ~4.2s; animated particles flow along causal edges, with confounder-to-X flow suppressed under intervention. Blocky elbow edges and rectangular arrowheads maintain the homebrew aesthetic; responsive scaling uses scale = min(w,h)/240 with grid size derived from scale.

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