tufte-causal-reasoning-in-graphicslisted

# Causal Reasoning in Graphics ## Overview Most data graphics describe; few argue. The difference is whether the display is organized around the causal variable or merely around time and sequence. Tufte's chapter "Visual and Statistical Thinking" in *Visual Explanations* establishes the standard through two paired case studies: John Snow's 1854 cholera map, which correctly displays a causal argument and helps end an epidemic, and the 13 Morton Thiokol charts faxed to NASA the night before the Challenger launch, which rested on the right causal theory but selected and arranged the data in ways that concealed the causal signal and contributed to seven deaths. The lesson is not stylistic: how data is arranged determines whether a causal relationship is visible or invisible, and that invisibility can be fatal. --- ## §1. The Fundamental Question: Compared with What? Every causal claim requires a comparison. Without a baseline, a count is only a count. Tufte identifies "Compared with what?" as the foundational question in statistical analysis. In *Visual Explanations* (p. 30), he argues that studying only cholera victims provides only half the evidence — a complete causal investigation requires equally rigorous analysis of those who did *not* contract the disease. Snow had 83 deaths mapped. The map's causal argument depended equally on showing the spaces with no deaths — the brewery (70+ workers, no cholera; they drank malt liquor and never used the Broad Street pump) and t