Workshop: Causal Machine Learning for Real-World Impact

Causal Analysis of the TOPCAT Trial: Spironolactone for Preserved Cardiac Function Heart Failure

Francesca Raimondi · Tadhg O'Keeffe · Hana Chockler · Andrew Lawrence · Tamara Stemberga · Andre Franca · Maksim Sipos · Javed Butler · Shlomo Ben-Haim


We describe the results of applying causal discovery methods on the data from a multi-site clinical trial, on the Treatment of Preserved Cardiac Function Heart Failure with an Aldosterone Antagonist (TOPCAT). The trial was inconclusive, with no clear benefits consistently shown for the whole cohort. However, there were questions regarding the reliability of the diagnosis and treatment protocol for a geographic subgroup of the cohort. With the inclusion of medical context in the form of domain knowledge, causal discovery is used to demonstrate regional discrepancies and to frame the regional transportability of the results. Furthermore, we show that, globally and especially for some subgroups, the treatment has significant causal effects, thus offering a more refined view of the trial results.

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