The code in this repository reproduces the results in [1].
This package depends on the dare
package, which implements the loss function
and interfaces deeptrafo
and deepregression
for stochastic gradient descent
via tensorflow
and keras
. The dare
package can be installed from
here.
All results can be reproduced by running make all
or executing the scripts in
./inst/code/
manually following the order in the Makefile
. The results can
also be reproduced in parts. For the 401k application, make 401k
; for the
schooling application, make schooling
; for the simulation results, make run-simulations vis-simulations
; Figure 3 can be reproduced with make loss-landscape
; and for all other figures use make figures
.
[1] Kook, L., & Pfister, N. (2024). Instrumental Variable Estimation of Distributional Causal Effects. arXiv preprint arXiv:2406.19986. doi:10.48550/arXiv.2406.19986.