Let $\alpha$ and $\beta$ be equivalent probability measures on $(\Omega, \mathcal{F})$, with Radon-Nikodym density of $\alpha$ wrt $\beta$ is $\eta$, i.e., for all $A \in \mathcal{F}, \beta(A) = \int_A\eta d\alpha$. Let $\mathcal{G}$ be a sub-$\sigma$-field of $\mathcal{F}$.
Show that $\eta$ is $\mathcal{G}$-measurable $\iff$ for any bounded random variable $X$, $E_{\alpha}[X|\mathcal{G}] = E_{\beta}[X|\mathcal{G}]$
I think I have the forward direction:
Let $\eta$ be $\mathcal{G}$ measurable. Using the General Baye's theorem: $$E_{\beta}[X|\mathcal{G}] = \frac{E_{\alpha}[\eta X|\mathcal{G}]}{E_{\alpha}[\eta|\mathcal{G}]}$$ However, $\eta$ is $\mathcal{G}$ measurable, so we have $E_{\alpha}[\eta X|\mathcal{G}] = \eta E_{\alpha}[X|\mathcal{G}]$ and $E_{\alpha}[\eta|\mathcal{G}] = \eta$, so $$E_{\beta}[X|\mathcal{G}] = \frac{E_{\alpha}[\eta X|\mathcal{G}]}{E_{\alpha}[\eta|\mathcal{G}]}$$ $$ = \frac{\eta E_{\alpha}[X|\mathcal{G}]}{\eta} = E_{\alpha}[X|\mathcal{G}]$$
I am just confused about the backward direction, I can't seem to get it. Also, I'm not sure I've done the forward direction in the best way. Thanks!
You didn't apply Bayes formula correctly; it should read
$$\mathbb{E}_{\color{red}{\beta}}(X \mid \mathcal{G}) = \frac{\mathbb{E}_{\color{red}{\alpha}}(\eta X \mid \mathcal{G})}{\mathbb{E}_{\color{red}{\alpha}}(\eta \mid \mathcal{G})}. \tag{1}$$
If you switch the roles of $\alpha$ and $\beta$ in your reasoning, then everything will work fine.
Regarding the backward direction: