njawadekar
njawadekar
The below post is more of a methodological question than a technical one. Based on what I've gathered, the _honest causal forest_ and _policy tree_ are two distinct yet related...
In [this tutorial](https://bookdown.org/halflearned/tutorial/pl1.html#using-policytree), it is described how one can evaluate a policy tree which is generated in an _experimental_ setting: `# Only valid for experimental setting!` `# predicting pi(X[i]) for...
I notice much variation in the variables that are selected by the policytree, depending on the size of the random training subset that is specified, in this step: #### Fit...
I am using policytree, version 1.2.0, and I am specifically trying to implement policy learning on the doubly robust reward estimates derived from an honest causal forest (GRF package). While...
Based on existing [documentation](https://cran.r-project.org/web/packages/policytree/policytree.pdf), I understand that when outputting a policy tree object into a visualization using the **plot.policy_tree** function, one can add the _leaf.labels_ argument to ensure that the...
In layman's terms, can you please explain the process through which the policy_tree function inputs the double_robust_scores (i.e., the Gamma.matrix from causal forest that is interpreted as rewards), and uses...
To handle missing data in covariates that will be ultimately used in BART, is there a particular imputation technique (if any) or a standard protocol that you recommend we use...
Hello, I am interested in using the bartcause package for causal inference using a time-to-event binary outcome. In other words, using BART, I would like to estimate CATEs that are...