Varying adaptive weighting function

[liaochris]

Hi! I am trying to apply causal forest to a panel data setting. I have two questions in bold.

Is there a way to modify the adaptive weighting function ($\alpha_i(x)$ from equation 2 of Athey et. al 2019) such that it varies by categorical attributes in the data? The reason is that since I'm interested in estimating the CATE for $Y_{i}$ (relative to the pre-treatment period $g$) in multiple time periods ($Y_{i, s} - Y_{i,g}, Y_{i, s+1} - Y_{g},...Y_{i, s+k} - Y_{g}$), and I want to ensure that when the CATE for $\delta_{s-g}(x)$, one of my parameters of interest is calculated, that only observations from where $t = s$, not $t = s+1$ have non-zero weights (according to the adaptive weighting function).

This is to ensure that when comparing the treatment and control, I'm only comparing observations with the same $t-g$ value.

Please let me know if anything is unclear! I had originally thought of using a separate causal forest for each $Y_{i, t} - Y_{i,g}$ for $t \in [s, \cdots, s+k]$ but since I want to cluster SEs at the individual level inference is difficult under that procedure. Is there a way to combine each iteration of a bootstrapped CATE from multiple forests (assuming bootstrap seeds are the same in all forests) for the purposes of inference?