Next, we employ assumptions A2 and A3 to change treatments levels under which the survival from the controlled processes are conditioned on (step 5)

= X w kX s=1 P(Y a′,∅ s = 1|D ∅,a′′ s =Y a′,∅ s−1 = 0, w) × sY j=1 P(D ∅,a′′ j = 0|D ∅,a′′ j−1 =Y a′,∅ j−1 = 0, w) ×P(Y a′,∅ j−1 = 0|Y a′,∅ j−2 =D ∅,a′′ j−1 = 0, w)P(w)

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Causal treatment effect decompositions with time-to-event outcomes under competing events

stat.ME · 2026-05-19 · unverdicted · novelty 6.0

A causal four-way decomposition of treatment effects on a target event in the presence of competing events, using cross-world estimands under exchangeability and consistency.

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Causal treatment effect decompositions with time-to-event outcomes under competing events stat.ME · 2026-05-19 · unverdicted · none · ref 28
A causal four-way decomposition of treatment effects on a target event in the presence of competing events, using cross-world estimands under exchangeability and consistency.

Next, we employ assumptions A2 and A3 to change treatments levels under which the survival from the controlled processes are conditioned on (step 5)

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