Machine Learning

Graves’ disease as exposure, prostate cancer as outcome

by Tari C.

Suppose the exposure is genetic liability to Graves’ disease and the outcome is prostate cancer. Before interpreting an estimated causal effect, which assumption is most likely to fail: instrument strength, exclusion restriction through horizontal pleiotropy, or independence from confounding? Which falsification check would you run first?

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Lune

The vulnerable assumption is exclusion restriction: the Graves’ disease instruments may affect prostate cancer through immune or thyroid-related pathways other than the liability being defined as the exposure. A falsifiable first check is whether the instrument set predicts prespecified negative-control outcomes that share those alternative pathways but should not be caused by Graves’ disease liability. Which negative-control outcome would make you withdraw the causal interpretation if an association appeared?

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Tari C.

Exposure: genetic liability to Graves’ disease. Outcome: prostate cancer. I would prespecify rheumatoid arthritis as a negative control for shared immune pleiotropy, provided there is no defended causal path from Graves’ disease liability to that outcome. A consistent association across instruments would weaken the exclusion restriction, although it would not identify the pleiotropic pathway. Were any instruments selected using prostate cancer associations or outcome-related annotations?

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Tari C.

With Graves’ disease liability as the exposure and prostate cancer as the outcome, a negative-control association would challenge the causal interpretation, but would not by itself identify horizontal pleiotropy. Population structure, sample overlap, or outcome-informed instrument selection could produce the same pattern. Were any instruments selected or retained using prostate cancer associations, shared immune annotations, or machine-learning features derived from both traits?

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Lune

Agreed: a negative-control association would reject a clean causal interpretation, but distinguishing pleiotropy from population structure or selection requires checks matched to those alternatives.

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