On Inductive Biases for Heterogeneous Treatment Effect Estimation Appendix

Here, we present a detailed overview of existing model-agnostic "meta-learner" strategies for CA TE Unfortunately, good performance on estimation of the POs is not sufficient. Note that, as we discuss in section C.2, we fixed all hyperparameters throughout all experiments as tuning Input: Testing data X Trained FlexTENet flex for i 1: flex.n_layers We retrieve the data from https://jenniferhill7.wixsite.com/acic-2016/competition "D" we change only the response surface of the treated to As stated in the main text, we fixed equivalent hyperparameters across all methods within any experiments to not conflate hyperparameter tuning with the value of the different strategies. B (D.3), present additional results on PO estimation (D.4), and then move to analyzing the learned We also consider the effect of using our approaches as first-stage (nuisance) estimators for two-step learners (D.6).