than $Ch_{q}(e_{A})$.

The role of chance in decision provides the second reason. Just as the chance of $e$ tells you everything you need to know to figure out how strongly to believe $e$, the causal dependence of $e$ on $d$ tells you everything you need to know about $e$ and $d$ when deciding whether to do $d$ (assuming you are not indifferent about $e$). As Huw Price \[2012\] put it, causal dependence should be regarded as an analyst-expert about the conditional credences required by an evidential decision maker.

Consider the situation of a hypothetical agent Bob at $p^{\prime }$ deciding whether to act by affecting what happens in $\mathbf{2}$ to try to get outcome $e_{A}$ in $\mathbf{1}$. Bob can choose not to measure anything, or he can choose to measure polarization with respect to any axis $b$. If he were to measure nothing, $Ch_{q}(e_{A})$ would be $\frac12$. If he were to measure polarization with respect to the same axis as Alice, then $Ch_{q}(e_{A})$ would be either 0 or 1, with an equal chance (at his momentary location $p^{\prime }$) of either outcome. Since he can neither know nor affect which of these chances it will be, he must base his 